Drowsiness Level Estimation Device, Apparatus Including Drowsiness Level Estimation Device, and Air-Conditioning Apparatus

This application is a U.S. national stage application of PCT/JP2023/002795 filed Jan. 30, 2023, the contents of which are incorporated herein by reference.

The present disclosure relates to a drowsiness level estimation device that estimates a drowsiness level that is the level of drowsiness of a user, an apparatus including the drowsiness level estimation device, and an air-conditioning apparatus.

There is a drowsiness level estimation device that estimates the drowsiness level of a user based on indoor temperature information acquired by a temperature detection unit, an action that shows drowsiness and is determined based on a user image acquired by an imaging unit, the surface temperature of the user, and a database associated with age or sex (see, for example, Patent Literature 1). This drowsiness level estimation device estimates the drowsiness level under the assumption that the user rests the chin on the hand or takes other actions when the drowsiness level increases.

Patent Literature 1: Japanese Unexamined Patent Application Publication No. 2019-082282

The drowsiness level estimation device of Patent Literature 1 estimates the drowsiness level under such pre-assumption that the drowsiness level increases when the user rests the chin on the hand. However, the drowsiness level does not always increase when the user rests the chin on the hand. The drowsiness level estimation device of Patent Literature 1 has plenty of room for improvement to increase the estimation accuracy.

To solve the above problem, an object is to provide a drowsiness level estimation device that can estimate a drowsiness level with high accuracy, an apparatus including the drowsiness level estimation device, and an air-conditioning apparatus.

A drowsiness level estimation device according to an embodiment of the present disclosure includes a Doppler sensor configured to detect a pulse wave of a user, and an analysis unit configured to analyze the pulse wave detected by the Doppler sensor. The analysis unit is configured to generate an index value by digitizing the pulse wave based on chaos analysis in which a source of analysis is a pulse waveform shift that is a time-series shift of a waveform of the pulse wave, estimate a drowsiness level of the user based on the index value, and output drowsiness level information related to the estimated drowsiness level.

An apparatus according to another embodiment of the present disclosure includes the drowsiness level estimation device described above, and a controller configured to control an operation of an apparatus body based on the drowsiness level information output from the drowsiness level estimation device.

An air-conditioning apparatus according to still another embodiment of the present disclosure includes the drowsiness level estimation device described above, an air-conditioning unit configured to condition air in an indoor space, and a controller configured to control the air-conditioning unit based on the drowsiness level shown by the drowsiness level information output from the drowsiness level estimation device.

According to the embodiments of the present disclosure, the drowsiness level estimation device, the apparatus including the drowsiness level estimation device, and the air-conditioning apparatus can estimate the drowsiness level based on the index value generated based on the time-series shift of the waveform of the pulse wave detected by the Doppler sensor. The pulse wave is vital data related to heart pulsation and furthermore activities in a brain nervous system. The drowsiness level estimation device estimates the drowsiness level based on the index value obtained by digitization based on the pulse wave. Therefore, the drowsiness level can be estimated with high accuracy.

Embodiments 1 to 4 of the present disclosure are described below with reference to the drawings. In the drawings, the same or corresponding parts are denoted by the same reference signs and their description is omitted or simplified as appropriate.

1 FIG. 2 FIG. 3 FIG. 1 1 10 10 1 1 is a block diagram illustrating the configuration of a drowsiness level estimation deviceaccording to Embodiment 1 and the use configuration of the drowsiness level estimation device.is a schematic diagram of an antenna surface of a Doppler sensoraccording to Embodiment 1.is a schematic diagram of a board component mounting surface of the Doppler sensoraccording to Embodiment 1. The drowsiness level estimation deviceestimates the drowsiness level of a user. The drowsiness level estimation deviceshows the drowsiness level in the numerical form to objectify the drowsiness level.

1 10 103 10 10 The drowsiness level estimation deviceincludes the Doppler sensorand an analysis unit. The Doppler sensoremits a constant sinusoidal radio wave at about 24 GHz called “microwave band” or “submillimeter-wave band” toward a user whose drowsiness level is to be estimated. The body surface of the user is displaced by movement of blood vessels due to changes in a bloodstream along with heart pulsation. When the distance between the body surface of the user and the Doppler sensorchanges, the reflection wave reflected by the body surface of the user changes due to the Doppler effect.

10 10 10 The Doppler sensorreceives the reflection wave from the user in response to the movement of the blood vessels, and detects a pulse wave in the central nervous system of the user based on a frequency difference between the reflection wave and the transmission wave emitted from the Doppler sensor. The pulse wave is a waveform showing a change in movement of the body surface of a person due to heart pulsation, and includes a waveform of a change in movement of blood vessels and a waveform of a change in the body surface at the heart portion. The blood vessels run all over the body of the user, and the Doppler sensorcan detect the movement of the blood vessels in part of the body of the user, such as part of the head or arm, instead of the heart.

In distance measurement, a measurement frequency of 60 to 79 GHz is often used because of high resolution. However, the purpose in this case is detection of a pulse wave unlike great body movement or other movement, and it is necessary to analyze an infinitesimal fluctuation having an extremely low frequency characteristic of about 1 Hz. Therefore, analog detection using the 24-GHz Doppler system is suitable for the pulse wave detection.

10 10 10 The Doppler sensoris advantageous in that the pulse wave of the user can be detected using the above radio wave without contact. Thus, the Doppler sensorcan measure a wide range of the body surface of the user. The Doppler sensorcan measure vital data such as a pulse rate, a respiration rate, body movement, a sleep state, and an autonomic balance by analyzing peak intervals of the pulse wave, but is used to detect the pulse wave in Embodiment 1.

Pulse wave sensors include not only the Doppler sensor that is a non-contact sensor, but also a contact sensor that performs detection while being in contact with the user. There are many devices that measure pulses by contact. A photoelectric pulse wave sensor is often used as a wearable device. The pulse wave sensor detects a change in the volume of a blood vessel along with blood pumping from the heart as a waveform, and includes a detector that monitors the volume change. The pulse wave sensor can obtain a pulse interval by measuring an interval between the peaks of the obtained pulse wave. A pulse rate per minute can be calculated by determining an inverse of the pulse interval. For example, when the pulse interval is 800 ms (0.8 seconds) on average, the pulse rate is 75 per minute based on 60÷0.8.

The pulse wave sensors include a transmission type and a reflection type depending on a difference in measurement method. The transmission pulse wave sensor can measure a pulse wave by irradiating the body surface with an infrared ray or red light and measuring a change amount of light passing through the body as a change in the blood flow rate along with heart pulsation. However, the measurable portion for the transmission pulse wave sensor is limited to a portion through which the infrared ray or red light easily passes, such as a fingertip or an ear lobe.

The reflection pulse wave sensor irradiates a living body with an infrared ray, red light, or light having a green wavelength of about 550 nanometers, and measures light reflected in the living body using a photodiode or phototransistor. Oxyhemoglobin is present in artery blood, and has a characteristic that it absorbs incident light. Therefore, the reflection pulse wave sensor can measure a pulse wave signal by sensing, in time series, a blood flow rate that changes along with heart pulsation (change in pressure in the blood vessel). Specifically, the pulse wave sensor includes a light emitting element and a light receiving element. The light emitting element radiates light, and the light receiving element detects light reflected by a finger. The intensity of the light reflected by the finger shows an increase or decrease amount of hemoglobin flowing through a capillary vessel of the fingertip. The reflection pulse wave sensor can obtain time-series pulse wave data associated with the increase or decrease amount of hemoglobin (blood flow rate). Since the reflection pulse wave sensor performs measurement using the reflected light, there is no need to limit the measurement portion like the transmission pulse wave sensor.

1 10 10 1 10 1 However, the contact sensor such as the pulse wave sensor needs to perform measurement while being worn. Therefore, there are problems in that the wearing is bothersome and measurement cannot be performed remotely. In view of this, the drowsiness level estimation deviceuses the non-contact Doppler sensoras the sensor that measures the pulse wave of the user. With the Doppler sensor, the drowsiness level estimation devicecan acquire the pulse wave of the user without contact, and the user need not wear the sensor. Thus, data necessary for drowsiness level estimation can be acquired without bothering the user with the wearing of the sensor. With the Doppler sensor, the drowsiness level estimation devicecan measure the pulse wave in a wide range of the body surface of the user.

10 10 100 101 102 10 100 100 100 12 100 100 a a a. 2 FIG. The Doppler sensormixes an emitted signal with a received signal, extracts a variation component caused by the Doppler effect, and generates a pulse wave of an IQ signal. Specifically, the Doppler sensorincludes an antenna unit, a radio unit, an analog circuit unit, and a board unit. The antenna unitacquires a pulse wave of the user that is a central nerve activity. The antenna unitincludes an emission unit TX and a reception unit RX. As illustrated in, the antenna unitincludes a plurality of (in this case) antennas. TX and RX each include six antennas

101 102 102 3 FIG. The radio unitgenerates a radio wave at 24 GHz called “radio frequency (RF),” emits the radio wave from TX, and receives the reflection wave by RX. The analog circuit unitincludes a circuit unit that performs IQ detection on the reflection wave, converts the wave into an IQ signal, and converts a frequency component that is a Doppler change in the reflection wave. As illustrated in, the analog circuit unitincludes an analog amplification filter unit (OPAMP) that extracts and amplifies a necessary frequency band, and an analog-digital conversion unit (LDO) that enables numerical analysis.

10 103 1 101 102 103 a 3 FIG. 3 FIG. The board unitincludes a connector unit for outputting information to the analysis unitor an apparatus including the drowsiness level estimation device, and a memory. As illustrated in, the radio unit, the analog circuit unit, and the analysis unitare covered with a metal shield case. In, the shield case part is dotted.

103 10 103 103 103 103 104 106 The analysis unitanalyzes the pulse wave detected by the Doppler sensor. The analysis unitgenerates an index value by digitizing the pulse wave based on chaos analysis in which the source of analysis is a pulse waveform shift that is a time-series shift of the waveform of the pulse wave (hereinafter referred to as “pulse waveform”), and estimates the drowsiness level based on the index value. The drowsiness level estimation by the analysis unitis described later. The analysis unitoutputs drowsiness level information showing a drowsiness level estimation result. The drowsiness level information is information related to the drowsiness level. The drowsiness level information output from the analysis unitis input to a control detail determination unitor a cloud unitdescribed later.

103 103 103 103 103 103 10 10 The analysis unitis a microprocessor unit. The analysis unitincludes a central processing unit (CPU), a read only memory (ROM), and a random access memory (RAM). The ROM stores a control program or other data. The analysis unitis not limited to the microprocessor unit. For example, the analysis unitmay be firmware that can be updated. The analysis unitmay be a program module to be executed by a command from a CPU (not illustrated) or other devices. The analysis unitmay separately be provided outside the Doppler sensor, or may be provided on the board in the Doppler sensorto perform edge processing in the single sensor.

103 1 1 1 104 105 104 105 105 104 The drowsiness level estimated by the analysis unitcan be used to control an apparatus including the drowsiness level estimation device. The apparatus including the drowsiness level estimation deviceis, for example, an air-conditioning apparatus, and details thereof are described later in Embodiment 4. The apparatus including the drowsiness level estimation deviceincludes the control detail determination unitand an apparatus control unit. The control detail determination unitdetermines details of control on the apparatus based on the input drowsiness level information, generates control data, and outputs it to the apparatus control unit. The apparatus control unitcontrols various actuators of the apparatus based on the control data from the control detail determination unit.

1 103 106 106 103 103 10 106 106 107 106 108 106 As a use form of the drowsiness level estimation device, the drowsiness level information output from the analysis unitmay be input to the cloud unit. The cloud unitaccumulates the drowsiness level information input from the analysis unit. The analysis unitmay further output vital data that is pulse wave data obtained by the Doppler sensor, and the cloud unitmay accumulate it. The drowsiness level information and the vital data accumulated in the cloud unitcan be visualized when displayed on a display unit. The drowsiness level information and the vital data accumulated in the cloud unitneed not always be visualized, and may be provided to a data collection unitfrom another cloud via the cloud unitand utilized for various purposes.

107 107 The display unitis a display such as a liquid crystal display panel. The display unitmay be a display unit of a smartphone, and the drowsiness level information may be visualized by being displayed on an application installed in the smartphone.

In the conventional system that estimates the drowsiness level of a person from an image acquired by the imaging unit, the drowsiness level is estimated under such pre-assumption that the drowsiness level increases when the person rests the chin on the hand. However, the drowsiness level does not always increase when the person rests the chin on the hand. This system has room for improvement to increase the estimation accuracy. Hitherto, there is a device that estimates human emotions using an electroencephalograph, but a waves and p waves of brain waves are not easily quantified. The estimation device using the electroencephalograph has such problems in terms of practicality and analysis time that the estimation cannot be performed in real time because measurement data in a predetermined period is required for the estimation process, the electroencephalograph needs to be worn on the head, and the system is complicated.

1 10 The drowsiness level estimation deviceof Embodiment 1 can estimate the drowsiness level in a short time by the chaos analysis described below from the pulse wave detected by the Doppler sensorwithout contact.

Details of the chaos analysis are described. In physiopsychology, the physiological state and the psychological state of a person are estimated from a physiological change exhibited in a biometric signal. In the conventional physiopsychology, various biometric signals such as brain waves, an electrocardiogram, heartbeat intervals, blood pressure, and a respiration digital plethysmogram have been analyzed by various methods and many findings have been obtained. However, a majority of the analyses was mainly performed by analysis methods based on linear theory. However, the biometric signals include nonlinear characteristics and are known to vary due to nonlinear characteristics called “chaos.” Chaos refers to a phenomenon that the state transition rule of a system is deterministic but the nonlinearity of the system causes complexity equivalent to a stochastic system. The state of a target is deterministically described by, for example, an equation, but no rule is found in the phase of the state of the target and the phase shows a significantly complex behavior such as randomness.

Although the chaos phenomenon appears to have no order, this phenomenon definitely has a rule on the background in actuality. In other words, the phenomenon that will occur next is not determined by the probability but is deterministic in accordance with a predetermined rule. The reason why the target appears to have no order despite the rule is that the motions of elements of the target are simple but turn complex when they behave as an aggregate. The biometric signals generated from the complex system are determined to have a strong possibility that chaos information is present.

In recent years, various experiments have demonstrated effectiveness of chaos analysis in which the physiopsychological states of a person are estimated. In the conventional chaos analysis, information that is not determined uniquely, such as a thermal sensation or a psychological state of a person, is subjected to the chaos analysis as a chaos target, thereby finding a relationship from information that appears to have no relationship. In the chaos analysis, it is important to determine what is set as the chaos target. When the target changes, it leads to a different concept.

1 1 1 1 The chaos target of the drowsiness level estimation deviceis a brain activity in the central nervous system. Hitherto, the brain activity in the central nervous system has not been the chaos target. The drowsiness level is deeply related to the brain activity in the central nervous system. Therefore, the drowsiness level estimation devicesets the brain activity in the central nervous system as the chaos target, and estimates the drowsiness level based on the result of the chaos analysis. The drowsiness level estimation deviceperforms the chaos analysis in which the source of analysis is a pulse waveform fluctuation focusing on how the waveform is shifted. The pulse waveform fluctuation is represented by time-series data of a shift of the waveform of the pulse wave, in other words, time-series shift data of the waveform of the pulse wave. The drowsiness level estimation devicegenerates an index value by digitizing the pulse wave based on the chaos analysis in which the source of analysis is a pulse waveform shift that is the time-series shift of the waveform of the pulse wave, and estimates the drowsiness level of the user based on the index value.

(1) The first step is a step of calculating vectors determined from the time-series data of the pulse waveform shift and a preset delay time. (2) The second step is a step of generating an attractor having the vectors arranged in time series in a three-dimensional state space. (3) The third step is a step of calculating a Lyapunov exponent that is the index value based on trajectories of the attractor. The chaos analysis has the following three sequential steps (1) to (3). Details of the steps are described later.

The heart pulsation interval varies at every heartbeat. The variation originates from the brain and communication is made to the heart through the autonomic nerve. The inventors have considered that, in the drowsiness level estimation, the central nervous system as well as the autonomic nervous system is related to the heart pulsation, and conceived that the drowsiness level is finally estimated by finding a correlation between the heart pulsation and the brain activity level.

Hitherto, there is a technology of estimating a brain arousal level using a pulse change. The brain arousal level can be regarded as an index inverse to the drowsiness level. In the technology of estimating the brain arousal level, for example, determination is made that the arousal level is high when the pulse change is large, and that the arousal level is low and drowsiness occurs when the pulse change is small. The pulse change is a time variation in the interval between the peaks of the pulse wave, and is detected using only peak information of the pulse wave with pinpoint accuracy. The time variation in the interval between the peaks of the pulse wave may be hereinafter referred to as “one-dimensional pattern pulse shift” or “pulse fluctuation.” The term “one-dimensional” is used because the height of the pulse wave is not related to the time variation in the interval between the peaks of the pulse wave and vital data is obtained by frequency conversion using only the shift of the pulse interval.

Although the conventional technology of measuring the arousal level uses the pulse change, the inventors have considered that information on the complex nerve activity of the living body is not fully determined from the pulse change alone. The inventors have sought for a method different from the pinpoint detection using only the peak information of the pulse wave, and focused on the shift of the pulse waveform. Comparing the complexity of the peak interval fluctuation with the complexity of the two-dimensional waveform pattern fluctuation from waveform to waveform in the pulse wave, however, the two-dimensional waveform pattern fluctuation (i.e., pulse waveform fluctuation) is much more complex than the one-dimensional pulse interval fluctuation. This may be because the pulse waveform fluctuation is related to neuron activities of six cerebral cortices. Therefore, the inventors use, instead of the conventional analysis method, chaos as the method for analyzing the brain activity and furthermore the drowsiness level.

103 1 103 10 10 4 FIG. Next, the chaos analysis to be performed by the analysis unitof the drowsiness level estimation deviceis described. First, the analysis unitacquires a pulse wave from the Doppler sensor.illustrates an example of the pulse wave detected by the Doppler sensor.

4 FIG. 4 FIG. 4 FIG. 10 10 103 10 10 is a diagram illustrating an example of the pulse wave detected by the Doppler sensoraccording to Embodiment 1. In, the horizontal axis represents time and the vertical axis represents an analog pulse wave height. The pulse wave height is power. The term “pulse wave height” does not refer to power at the peak where the power of the pulse wave is highest, but refers to time-based power in time series including the peak of the pulse wave. The Doppler sensorobtains the analog waveform illustrated inand outputs it to the analysis unit. The pulse wave height decreases as the distance between the Doppler sensorand the measurement target user increases. In the chaos analysis of Embodiment 1, however, the shift of the pulse wave height is analyzed and therefore the absolute value of the pulse wave height is not necessary information in the pulse wave digitization. However, the pulse waveform is clearer when the pulse wave height increases, and the accuracy of attractor generation described later increases. Thus, the Doppler sensoris preferably closer to the user.

103 103 103 103 10 The analysis unitmay perform the following to obtain a pulse wave height necessary to secure the accuracy in the analysis of the time-series data of the pulse waveform. The analysis unitcalculates a deviation of the pulse wave height. When the deviation is smaller than a preset threshold, the analysis unitautomatically adjusts the input signal amplification factor to virtually increase the size of the analog pulse waveform. In this manner, the shape of the pulse wave is clearer and the analysis unitcan perform highly accurate analysis even if the Doppler sensoris located away from the user.

10 10 103 103 103 103 10 103 10 For example, when the Doppler sensoris close to the user, the input signal multiplication factor may be “1.” As the distance between the Doppler sensorand the user increases, the size of the pulse wave decreases, the shift of the pulse waveform is more unclear, and the deviation decreases. Therefore, the analysis unitmay determine the multiplication factor depending on how much the deviation is small. For example, when the deviation is smaller than a first threshold, the analysis unitmay set the input signal multiplication factor to “2,” and when the deviation is smaller than a second threshold that is smaller than the first threshold, the analysis unitmay set the input signal multiplication factor to “3.” That is, the analysis unitincreases the size of the analog waveform by automatically increasing the input signal amplification factor as the distance between the Doppler sensorand the user increases. Thus, the analysis unitcan analyze the pulse waveform fluctuation (time-series shift data of the pulse waveform) even if the Doppler sensoris located away from the user.

10 103 103 When the area of a child user or other users and the power of the bloodstream to blood vessels are small even at the same distance between the Doppler sensorand the user, the analysis is difficult because the pulse wave level is low. Therefore, the analysis unitincreases the size of the analog waveform by automatically increasing the input signal amplification factor as the area of the user decreases and the deviation of the pulse wave height decreases. Thus, the analysis unitcan solve the problem that the analysis is difficult when the pulse wave level is low.

103 Next, the analysis unitgenerates an attractor described later from the pulse waveform. The pulse waveform is a two-dimensional waveform pattern of the pulse wave. Although it is difficult to find a rule in the time-series data of the pulse waveform from waveform to waveform in the pulse wave, a predetermined pattern is present when the time-series data of the pulse waveform is converted into the attractor. The attractor is an aggregate in which a certain dynamical system temporally develops toward it. When motion is performed in the certain dynamical system from a point sufficiently close to the attractor, the system remains sufficiently close to the attractor. The trajectory included in the attractor has no limitation except that it remains in the attractor.

103 Next, the first to third steps of the chaos analysis to be performed by the analysis unitare described in sequence. Embodiment 1 is characterized in that the brain activity in the central nervous system is the chaos target and the source of the chaos analysis is the pulse waveform shift that is the time-series shift of the waveform of the pulse wave. A conventionally known method is used as the method for the chaos analysis. Therefore, the chaos analysis is outlined below.

As described above, the first step is the step of calculating vectors determined from the time-series data of the pulse waveform shift and the preset delay time.

5 FIG. 5 FIG. 1 10 is a diagram illustrating the time-series data of the pulse waveform shift in the drowsiness level estimation deviceaccording to Embodiment 1. In, the horizontal axis represents time and the vertical axis represents the analog pulse wave height. x(i) (i=1, 2, . . . , n) is the time-series data of the pulse waveform based on sensor data obtained by the Doppler sensor. The difference between the pulse wave height at x(i) and the pulse wave height at x(i+1) is the pulse waveform shift. x(0) is an initial value of sensor data obtained within a time of a calculation window length. The calculation window length is the length of time set as appropriate, and is, for example, 20 seconds. The calculation can be performed more quickly as the calculation window length decreases. The amount of pulse waveform data increases and the accuracy increases as the calculation window length increases.

103 103 103 To embed the two-dimensional time-series change in a d-dimensional state space using the time-series data, in other words, to draw a locus in the d-dimensional state space, the analysis unitgenerates vectors while setting a time lag T as an appropriate delay time. Specifically, the analysis unitgenerates vectors X(i)={x(i), x(i+τ), x(i+2τ), . . . , x(i+(d−1)τ)}. For example, when d is “3” and the two-dimensional time-series change is embedded in a three-dimensional state space, the analysis unitgenerates vectors with three state variables. In the three-dimensional state space, the vectors X(i) are X(i)={x(i), x(i+τ), x(i+2τ)}. Since i=1, 2, . . . , n, n vectors X are generated. The parameter T is called “embedding delay time.”

The second step is the step of generating an attractor having the vectors arranged in time series in a multidimensional state space such as a three-dimensional or higher-dimensional state space.

6 FIG. 6 FIG. 6 FIG. 1 is a conceptual diagram of the attractor in the chaos analysis in the drowsiness level estimation deviceaccording to Embodiment 1. A trajectory is obtained when the vectors X(i) are sequentially plotted on coordinate axes x(i), x(i+τ), x(i+2τ), . . . , x(i+(d−1)τ).illustrates a three-dimensional state space and three coordinate axes x(i), x(i+τ), and x(i+2τ). For example, when the vectors obtained in the first step are plotted (arranged) in time series in the three-dimensional state space with d=3 and the delay time set to 0.05 seconds, a locus as illustrated inis obtained. This locus is the trajectory of the attractor. As for the shape of the trajectory of the attractor, a spiral locus is obtained. This demonstrates that chaos information is present in the fluctuation in the two-dimensional pattern pulse waveform.

The third step is the step of calculating a Lyapunov exponent that is an index value based on the trajectories of the attractor. The Lyapunov exponent is obtained by evaluating instability or divergence of the trajectories of the attractor.

7 FIG. 7 FIG. 1 is a conceptual diagram of Lyapunov exponentiation in the drowsiness level estimation deviceaccording to Embodiment 1.illustrates a case where the multidimensional state space is a three-dimensional state space. It is necessary to digitize the time-series shift of the pulse waveform to finally estimate the drowsiness level. The Lyapunov exponent is used as the index value obtained by the digitization. The attractor in the multidimensional state space such as a three-dimensional or higher-dimensional state space has instability of the trajectories. The instability can be rephrased as divergence. The Lyapunov exponent is obtained by quantifying the trajectory instability. The Lyapunov exponent is a measure of the distance between two trajectories departing from two nearby points. In other words, the Lyapunov exponent is a value showing the degree of separation of nearby trajectories in the dynamical system. As the Lyapunov exponent increases, the variation range of the attractor increases and the range of fluctuation increases.

7 FIG. In the calculation of the Lyapunov exponent, a microsphere (hypersphere) having a radius s is first given as an initial value to the trajectories of the attractor in a three-dimensional chaos dynamical system as illustrated in. Specifically, a sphere having the small radius s about a point on the trajectory of the attractor is set as the hypersphere on the trajectory. In the chaos analysis, points in the hypersphere are found on the trajectory of the attractor, and a change amount from each point to a point after an elapse of a slide time is linearly approximated. Thus, enlargement factors in e1, e2, and e3 directions are calculated. The slide time is a time interval from a previous sphere to a subsequent sphere. The hypersphere is initially a sphere. After an elapse of a preset slide time S, mapping is performed once so that the sphere is extended in the e1 direction, substantially unchanged in the e2 direction, and contracted in the e3 direction. As a result, the sphere becomes an ellipse. Assuming that λ1, λ2, and λ3 are logarithms of the enlargement factors per unit time in the e1, e2, and e3 directions, λ1, λ2, and λ3 are Lyapunov exponents.

The set of the Lyapunov exponents is called “Lyapunov spectrum.” In the chaos analysis, the operation of stretching the sphere is repeated at intervals of the slide time S and the enlargement factors are calculated. The sums are obtained and averaged so that an overall Lyapunov spectrum is calculated. The largest Lyapunov exponent among the calculated Lyapunov exponents is called “maximum Lyapunov exponent.” The maximum Lyapunov exponent shows the degree of separation of the trajectories compared with the initial state in the dynamical system. That is, the Lyapunov exponent shows the degree of separation in the n-th state compared with the initial state. As for the brain activity, the human brain works more actively as the degree of separation increases compared with the initial state, and the brain works inactively when the degree of separation remains unchanged compared with the initial state. Although the accuracy decreases, the Lyapunov exponents may be calculated by comparison between two states with the slide time regarded as one section instead of calculation performed multiple times at intervals of the slide time S.

When the slide time is reduced in the chaos analysis, the calculation accuracy is improved but the calculation time increases. When the number of points in the sphere is excessively large, the calculation time increases. Therefore, a nearby point count may be set as an upper limit value of the number of points in the sphere to reduce the calculation time. When the count of points in the sphere is larger than the set upper limit count, the calculation proceeds to the subsequent sphere.

103 Although the description has been made about the case where the multidimensional state space is the three-dimensional state space, the analysis unitcan generate the attractor in a three or higher-dimensional state space. When the multiple dimensions are five or more dimensions, the accuracy increases but the calculation time increases. Thus, practicality decreases. In view of the obtainment of chaoticness and the calculation time, the multiple dimensions are preferably three or four dimensions.

The description has been made that the Lyapunov exponent shows the degree of separation of the trajectories. The relationship between the degree of separation of the trajectories and the brain activity shows that, as the trajectories separate compared with the initial state, the human brain works actively and the brain activity level is high. When the trajectories remain unchanged compared with the initial state, the brain works inactively and the brain activity level is low.

Specifically, the Lyapunov exponent is calculated as follows. For example, it is assumed that the radius of the sphere is 0.08, the calculation window length is 20 seconds, the slide time is 1 second, and the measurement frequency is 200 Hz. Since the measurement frequency is 200 Hz, 200 pieces of data are obtained within 1 second, and 4000 pieces of data are obtained within the time of the calculation window length from the start of calculation. As for the Lyapunov exponent in the three-dimensional state space, a Lyapunov spectrum including λ1, λ2, and λ3 in three dimensions is obtained using 4000 pieces of data within 20 seconds from the start of measurement. A Lyapunov spectrum including λ1, λ2, and λ3 is similarly obtained within the next 1 second.

When an output window length is set to 60 seconds, the above operation is repeated for 60 seconds. That is, the first Lyapunov spectrum is obtained within the first 20 seconds, and a Lyapunov spectrum is obtained every 1 second of the slide time within the next 40 seconds. Thus, a total of 40 Lyapunov spectra are obtained. Then, the sums of λ1, λ2, and λ3 in the 40 Lyapunov spectra are obtained and averaged, and the averaged λ1, λ2, and λ3 are determined. The largest Lyapunov exponent among them is the maximum Lyapunov exponent. The maximum Lyapunov exponent is one piece of data obtained within the output window length. That is, the maximum Lyapunov exponent is employed as the degree of separation compared with the initial value in the system.

103 103 The number of measured pieces of pulse wave data that is vital data is discussed. For example, when the measurement frequency is set to 200 Hz and the output window length is set to 60 seconds, 12000 pieces of data are obtained within 60 seconds. The analysis unitgenerates the trajectory of the attractor using the 12000 pieces of data, and calculates the Lyapunov exponent. As described above, the output window length is a time necessary to output one piece of data, and is a time necessary to calculate one Lyapunov exponent. For example, when the measurement frequency is set to 500 Hz and the output window length is set to 60 seconds, 30000 pieces of data are obtained within 60 seconds. In this case, the analysis unitgenerates the trajectory of the attractor using the 30000 pieces of data, and calculates the Lyapunov exponent.

As the number of pieces of data increases, there is an advantage in that the accuracy increases. When the number of pieces of data is large, however, there are disadvantages in that the measurement time increases, the drowsiness level change cannot be followed, and the CPU processing time increases or calculation cannot be performed. As the number of pieces of data decreases, the accuracy decreases. When the number of pieces of data is small, however, there are advantages in that the measurement time decreases, the CPU processing time decreases, and the followability is good.

103 103 103 103 103 Thus, the measurement frequency is preferably about 100 Hz to 1000 Hz. The output window length is preferably 30 seconds or more and less than 5 minutes. Since the response time in the central nervous system is short, the output window length is preferably shorter than in the case of measurement in the autonomic nervous system, that is, preferably 30 seconds or more and 60 seconds or less. The analysis unitmay perform the following to increase the accuracy. The analysis unitmay collect one output piece of data in every set time and perform averaging. For example, when the output window length is 30 seconds and the set time is 5 seconds, the analysis unitcollects, at a timing of 5 seconds, one piece of data output based on vital data in 30 seconds before the timing. When 30 pieces of data are collected, the analysis unitmay obtain one piece of data by averaging the 30 pieces of data. In this case, the analysis unitcan obtain one piece of highly accurate data (Lyapunov exponent) taking 3 minutes for digitization.

8 FIG. 8 FIG. Next, the correlation between the “Lyapunov exponent” found by the inventors and the “brain activity in the central nervous system” deeply related to the drowsiness level is described with reference to.is a diagram created based on results of experiment in which the user performs various behaviors different in brain activities and Lyapunov exponents are calculated based on pulse waves acquired from the user performing the behaviors.

8 FIG. is a diagram illustrating, in the form of a bar graph, the relationship between the Lyapunov exponents and the various behaviors different in brain activities in the central nervous system. Numerical values on the vertical axis are values normalized with a rest set to “1.” That is, the numerical values on the vertical axis in the behaviors other than the rest are values obtained by dividing the Lyapunov exponents of the behaviors by the Lyapunov exponent of the rest. The horizontal axis represents human behaviors with different brain activity levels. The following four behaviors are employed as the behaviors on the horizontal axis. The four behaviors are “taking rest,” “reading news,” “preparing mail,” and “transcribing document by typing.” For example, “reading news” refers to a behavior of reading a document on a personal computer or a smartphone. “Transcribing document by typing” refers to a behavior of typing texts in a document on a personal computer.

8 FIG. demonstrates that the numerical value on the vertical axis is larger in the heavy work such as “preparing mail” or “transcribing document by typing” in which the brain activity level is high even according to the user's subjective report than in the light work such as “taking rest” or “reading news.” That is, the Lyapunov exponent is larger when the brain activity level is higher. Thus, the brain activity level in the central nervous system and the Lyapunov exponent have a relationship.

8 FIG. During the rest, the drowsiness level is higher than during the transcription of a document by typing. This is also shown in the user's subjective report. Thus, the behavior, the Lyapunov exponent, and the drowsiness level have a correlation. The Lyapunov exponent may be handled also as a mental index that is the drowsiness level. Specifically, evaluation may be made that the drowsiness level is low when the Lyapunov exponent is large and the drowsiness level is high when the Lyapunov exponent is small. Thus, the vertical axis ofcan be regarded also as an index of the drowsiness level. That is, the drowsiness level is lower when the numerical value on the vertical axis is larger, and the drowsiness level is higher when the numerical value on the vertical axis is smaller. Thus, it is presumed that the numerical value on the vertical axis may be regarded as, for example, a drowsiness exponent showing the drowsiness level.

9 FIG. The inventors have installed the Doppler sensor near the working user, and continuously calculated the Lyapunov exponents based on results of continuous measurement of the pulse wave of the user. The inventors have calculated the Lyapunov exponents in the user's subjective report that “he/she had drowsiness” that occurred several times a day, for example, after lunch, and in the user's subjective report that “he/she had no drowsiness.”illustrates results of calculation of the Lyapunov exponents.

9 FIG. 9 FIG. 9 FIG. 9 FIG. is a diagram illustrating an example of changes in the Lyapunov exponent during work. The horizontal axis represents elapsed time (minute), and the vertical axis represents the Lyapunov exponent at that time.also illustrates results of the drowsiness level in the user's subjective report.is a graph obtained by plotting the results of the Lyapunov exponent calculated at intervals of 1 minute.demonstrates changes in the Lyapunov exponent in response to changes in the user's subjective report on the drowsiness level, showing a “not drowsy” state, a “very drowsy” state, and a “not drowsy” state in this order. The elapsed times of the “not drowsy,” “very drowsy,” and “not drowsy” states are continuous times of about 12 minutes, 15 minutes, and 7 minutes, respectively.

9 FIG. demonstrates that, in comparison between the “not drowsy” state and the “very drowsy” state, the Lyapunov exponent is large in the “not drowsy” state and small in the “very drowsy” state. That is, the value of the Lyapunov exponent is large when the drowsiness level is low, and is small when the drowsiness level is high. The Lyapunov exponent gradually decreases when the “not drowsy” state changes to the “very drowsy” state. The Lyapunov exponent gradually increases when the “very drowsy” state changes to the “not drowsy” state.

107 The above measurement results demonstrate that the Lyapunov exponent and the drowsiness level have a correlation. When the drowsiness level increases during work, the work efficiency decreases. Therefore, the measurement results can be utilized by, for example, being displayed on the display unitto alert the user and prompt the user to take a rest depending on the drowsiness level.

9 FIG. 9 FIG. 9 FIG. 9 FIG. 9 FIG. 1 1 1 1 The values of the Lyapunov exponent on the vertical axis ofare obtained by calculation using the drowsiness level estimation devicetemporarily as an experimental device.demonstrates that the Lyapunov exponents are obtained at intervals of 1 minute by the drowsiness level estimation device.also demonstrates that the process of calculating the Lyapunov exponents from the pulse wave can be performed in 1 minute that is generally a short time. Althoughillustrates the experiment results obtained using the drowsiness level estimation device,illustrates the experiment results of measurement of the correlation between the Lyapunov exponent and the drowsiness level, and may be created using a device dedicated to experiment instead of the drowsiness level estimation device.

107 The inventors have installed the Doppler sensor near the user before and after sleep, and continuously measured the Lyapunov exponents. The result of comparison between the Lyapunov exponent in the subjective report that “the user had drowsiness” before the sleep and the Lyapunov exponent in the subjective report that “the user had no drowsiness” demonstrates that the Lyapunov exponent is smaller, even before the sleep, when the user had drowsiness than when the user had no drowsiness. That is, the drowsiness level and the Lyapunov exponent before the sleep have a correlation. When the drowsiness level increases before the sleep, the user can smoothly fall asleep. Therefore, the measurement results can be utilized by, for example, being displayed on the display unitto notify the user and prompt the user to go to bed depending on the drowsiness level.

As described above, the Lyapunov exponent and the drowsiness level have the direct correlation, and the Lyapunov exponent may be handled also as a mental index that is the drowsiness level. Specifically, evaluation can be made that the drowsiness level increases as the Lyapunov exponent decreases and the drowsiness level decreases as the Lyapunov exponent increases. This method does not require linking to a large amount of past data and storage of complex data, and the drowsiness level can be evaluated easily in real time.

1 In view of the above relationship, the drowsiness level estimation deviceestimates the drowsiness level based on the Lyapunov exponent.

10 FIG. 1 1 1 2 21 23 21 22 23 is a flowchart of the drowsiness level estimation process in the drowsiness level estimation deviceaccording to Embodiment 1. The drowsiness level estimation deviceperforms a step of acquiring pulse waveform data (Step S), and a step of performing chaos analysis based on the pulse waveform data (Step S). As described above, the chaos analysis step includes three steps (Steps Sto S). The first step is a step of calculating vectors determined from time-series data of a pulse waveform shift and the preset delay time (Step S). The second step is a step of generating an attractor having the vectors arranged in time series in a three-dimensional state space (Step S). The third step is a step of calculating a Lyapunov exponent that is an index value based on trajectories of the attractor (Step S).

103 3 103 103 103 103 103 103 The analysis unitestimates a drowsiness level based on the Lyapunov exponent (Step S), and outputs drowsiness level information showing the estimation result. The analysis unitestimates that the drowsiness level increases as the Lyapunov exponent decreases and the drowsiness level decreases as the Lyapunov exponent increases. The analysis unitmay output the Lyapunov exponent as the drowsiness level information, or may, for example, convert the Lyapunov exponent into a grade value (e.g., 1 to 10) showing the drowsiness level and output the grade value. The grade value is, for example, a numerical value showing the drowsiness level in ascending order along with the change in the numerical value in ascending order. For example, the analysis unitmay hold the Lyapunov exponent during rest, and output, as a numerical value showing the drowsiness level, a numerical value of the Lyapunov exponent obtained by analysis in the analysis unitand expressed in percentage (%) of the Lyapunov exponent during the rest. The analysis unitneed not always output the drowsiness level as a numerical value as described above, and may output, for example, a grade image. The grade image refers to, for example, a facial expression image that varies depending on the grade of the drowsiness level. As described above, the analysis unitoutputs the Lyapunov exponent as the drowsiness level information showing the drowsiness level.

103 107 106 107 1 103 103 108 106 103 107 106 103 104 1 The drowsiness level information output from the analysis unitis input to the display unitvia the cloud unitand displayed on the display unit. Since the drowsiness level estimation devicevisualizes the drowsiness level information by outputting it from the analysis unit, the user can grasp the drowsiness level. The drowsiness level information output from the analysis unitmay be accumulated in the data collection unitvia the cloud unit. The drowsiness level information output from the analysis unitmay directly be input to and displayed on the display unitwithout intermediation of the cloud unit. The drowsiness level information output from the analysis unitmay be input to the control detail determination unitand used to determine details of control on the apparatus including the drowsiness level estimation device.

The drowsiness level information may be information showing a current drowsiness level, time-series drowsiness levels, or both of them.

1 103 As described above, when the drowsiness level in the subjective report is high, the brain activity level is low and the Lyapunov exponent is low. Thus, the drowsiness level estimation deviceis suitable when the analysis unitoutputs information showing that the drowsiness level increases as the Lyapunov exponent decreases.

103 The drowsiness level can be regarded also as an index inverse to the concentration level. Therefore, the analysis unitmay output concentration level information instead of the drowsiness level information. Thus, the user can view the concentration level.

1 Although the description has been made that the drowsiness level estimation devicecan output the drowsiness level information from the index value based on the brain activity amount estimation result, the term or expression “drowsiness level” is not limitative, and any other term or expression may be used as long as the human emotion or mentality and the behaviors with high brain activity levels have the same meanings. For example, the “drowsiness level” may be rephrased as “boredom level” or “laziness level.” Thus, the information obtained from the brain activity amount may be expressed as “boredom level” or “laziness level” instead of “drowsiness level.”

1 10 103 10 103 As described above, the drowsiness level estimation deviceof Embodiment 1 includes the non-contact Doppler sensorthat detects a pulse wave in a wide range of the body surface of the user using a radio wave, and the analysis unitthat analyzes the pulse wave detected by the Doppler sensor. The analysis unitgenerates an index value by digitizing the pulse wave based on the chaos analysis in which the source of analysis is a pulse waveform shift that is a time-series shift of the waveform of the pulse wave, and estimates the drowsiness level of the user based on the index value.

1 1 As described above, the drowsiness level estimation devicecan estimate the drowsiness level based on the index value generated based on the time-series shift of the waveform of the pulse wave. The pulse wave is vital data related to heart pulsation and furthermore activities in the brain nervous system. The drowsiness level estimation deviceestimates the drowsiness level based on the index value calculated based on the pulse wave. Therefore, the drowsiness level can be estimated with higher accuracy than in the conventional estimation method based on an action of resting the chin on the hand and other actions.

1 10 1 10 The drowsiness level estimation deviceuses the Doppler sensoras the sensor that detects the pulse wave of the user. Description is made about the superiority of the drowsiness level estimation devicethat estimates the drowsiness level based on the pulse wave detected using the Doppler sensorinstead of the contact pulse wave sensor.

Technologies disclosed in Japanese Unexamined Patent Application Publication Nos. 4-208136, 2009-195384, and 2015-16273 are technologies of detecting the psychosomatic state of a user using the pulse wave of the user. In each of the known technologies, the pulse wave is detected using a contact pulse wave sensor. Specifically, in each of the known technologies, the pulse wave sensor attached to the fingertip is used to measure the pulse wave by causing, for example, a photodiode to detect movement of hemoglobin flowing through a capillary vessel.

10 10 The contact pulse wave sensor captures the pulsing oscillation as a wave like the Doppler sensor, but the attachment portion is limited to, for example, the fingertip located away from the brain that is a main part of the brain activity to be used for the drowsiness level estimation. Therefore, the information acquired by the pulse wave sensor is limited compared with the information acquired by the Doppler sensor, and hardly shows the effect of the brain activity that is the central nerve activity.

1 10 10 10 1 The drowsiness level estimation devicedetects the pulsing oscillation of the blood vessels using the non-contact Doppler sensorwith the radio wave at 24 GHz or more. The Doppler sensorcan detect, using the radio wave, not only the pulsing oscillation of the body surface but also head information in the range that the radio wave reaches or the oscillation of the blood vessels in the body as well as the body surface. The Doppler sensorcan acquire pulse wave information of the entire body in the radio wave area. Therefore, the drowsiness level estimation deviceobtains more information on the autonomic nerve or the central nerve, thereby being advantageous in that the correlation accuracy in the analysis of the change in the pulse waveform increases.

1 10 10 1 As described above, the drowsiness level estimation devicecan acquire not only the information on the finger part located away from the brain but also the pulse wave information of the entire body including the head, shoulders, or chest using the Doppler sensor. With the Doppler sensor, the drowsiness level estimation devicecan measure the pulse wave affected more greatly by the brain activity that is the central nerve activity and furthermore the drowsiness level. As a result, the drowsiness level can be estimated with high accuracy.

10 10 10 The Doppler sensorcan detect fine oscillation of the entire body caused by heart pulsation. The Doppler sensorinternally mixes a transmitted signal with a received signal, extracts a variation component caused by the Doppler effect, and generates a pulse wave of an IQ signal. To be exact, the pulse wave information generated by the Doppler sensoris pulse wave information generated using the radio IQ signal unlike a plethysmogram obtained by the pulse wave sensor attached to the fingertip.

1 The above technologies of Japanese Unexamined Patent Application Publication Nos. 4-208136, 2009-195384, and 2015-16273 use chaos analysis of the pulse wave to detect the psychosomatic state of the user. However, the known technologies are different, in terms of finally obtained information, from the drowsiness level estimation devicethat estimates the drowsiness level of the user.

In Japanese Unexamined Patent Application Publication No. 4-208136, the body or psychological state of a test subject is obtained, such as “relaxed,” “reading book,” “reading comic book,” or “viewing beautiful picture.” In Japanese Unexamined Patent Application Publication No. 2009-195384, a fatigue level is obtained as the level of human fatigue. In Japanese Unexamined Patent Application Publication No. 2015-16273, a mental balance of a test subject is obtained. For example, information is obtained as to which of “ideal zone,” “too nervous,” “depressed,” “too lazy,” “on instinct,” and “semi-ideal zone” corresponds to the mental state of the test subject.

As described above, the known examples are different from the technology of estimating the drowsiness level of the user because the finally obtained information is different.

The chaos analysis is a process of sequentially performing a step of calculating vectors determined from time-series data of a pulse waveform shift and a preset delay time, a step of generating an attractor having the vectors arranged in time series in a multidimensional state space such as a three-dimensional or higher-dimensional state space, and a step of calculating, as a Lyapunov exponent that is an index value, an enlargement factor obtained by giving a hypersphere as an initial state to trajectories of the attractor and repeating an operation of stretching the hypersphere at intervals of the preset slide time based on the trajectories so that the hypersphere turns into an ellipse.

1 In this manner, the drowsiness level estimation devicecan calculate the Lyapunov exponent that is the index value by the chaos analysis.

103 The method that is used by the analysis unitto calculate the Lyapunov exponent from the attractor in the chaos analysis of the pulse wave is greatly different from a WORF method that is used in the above known examples of Japanese Unexamined Patent Application Publication Nos. 4-208136, 2009-195384, and 2015-16273.

In Japanese Unexamined Patent Application Publication No. 4-208136, a search is made for a next point from a start point of finally obtained two-dimensional data, and the Lyapunov exponent is calculated from the ratio between time and a movement distance. Japanese Unexamined Patent Application Publication No. 4-208136 uses the WORF method in which the Lyapunov exponent is obtained by repeatedly averaging, in the entire attractor, the Lyapunov exponents each calculated from the movement distance from point to point.

In Japanese Unexamined Patent Application Publication No. 2009-195384, an attractor is reconfigured in a predetermined time range on continuous data calculation values, and the time range is slid by 1 second each time so that the value of the maximum Lyapunov exponent is plotted every 1 second. Thus, the WORF method is similarly used in Japanese Unexamined Patent Application Publication No. 2009-195384.

In Japanese Unexamined Patent Application Publication No. 2015-16273, the WORF method is similarly used for a mental flexibility detection process.

The WORF method used in the known examples is briefly described below.

11 FIG. is an explanatory diagram of the WORF method used in the known examples. The WORF method described in Alan WOLF et al., DETERMINING LYAPUNOV EXPONENTS FROM A TIME SERIES, Physica 16D (1985) 285-317 is described in detail.

103 0 0 Δt The WORF method is simpler than the Lyapunov exponent calculation method of the analysis unit. A search is made for a point Pj located at a unit distance Lfrom a point Pi of interest in a chaos attractor, and the logarithm of the ratio between Land a distance Lbetween the points after a unit time Δt is calculated. In the WORF method, the logarithm calculation process is performed on the entire chaos attractor with Pi shifted, and a numerical value obtained by simply averaging the plurality of calculated logarithms is set as a Lyapunov exponent λ1. If the analysis target has periodicity, λ1 is 0, and if it is chaotic, λ1 is a positive value. In the WORF method, the Lyapunov exponent is calculated after the attractor is converted into a two-dimensional image. In the WORF method, an increase or decrease in the distance between the two points is calculated, and the information or behavior in the dynamical system is simple.

103 The Lyapunov exponent calculation method of the analysis unitis as described above. An enlargement factor is calculated from a temporal change in multidimensional directions in a microsphere (hypersphere) given as an initial state on trajectories in multiple dimensions that are three or more dimensions, and the extension or contraction of the three-dimensional object is calculated. Since the enlargement factor is calculated from how the hypersphere in the multiple dimensions develops continuously at each time, a large amount of information is obtained and the calculation accuracy increases. This point is the large difference from WORF.

In the known technologies, the Lyapunov exponent obtained by the WOLF method has a poor correlation with the brain activity and furthermore the drowsiness level. According to the experiment and research conducted by the inventors, there is no correlation with the brain activity.

103 With the above method in which the analysis unitperforms the Lyapunov exponentiation by giving the microsphere (hypersphere) to the multidimensional chaos dynamical system, the states with different brain activity amounts can be measured promptly, and the Lyapunov exponent can be obtained as an estimation index having a high linearity and a strong correlation with the brain activity amount and furthermore the drowsiness level.

103 The known examples are different from the present disclosure in terms of the relationship between the Lyapunov exponent and the brain activity obtained based on the user test results. Japanese Unexamined Patent Application Publication No. 4-208136 describes that the Lyapunov exponent decreases as the concentration increases, and the Lyapunov exponent decreases as the information processing in the brain is more active. That is, in Japanese Unexamined Patent Application Publication No. 4-208136, the Lyapunov exponent is a value that decreases as the brain activity level increases. The Lyapunov exponent that is the index value obtained by the analysis unitis a value that increases when the brain activity level increases, in other words, the drowsiness level decreases. The meaning of the Lyapunov exponent as the index value is completely opposite to that in the known example.

103 Japanese Unexamined Patent Application Publication No. 4-208136 describes that the attractor is output to a two-dimensional screen and the user is more relaxed as the size of the attractor decreases. In Japanese Unexamined Patent Application Publication No. 4-208136, the attractor is the two-dimensional attractor, and therefore sparseness and denseness are described about a local structure of the spiral of the attractor. When the concentration increases, the local structure changes from sparse to dense. However, the sparse/dense relationship of the attractor has no relationship with the Lyapunov exponent obtained by the analysis unit.

Japanese Unexamined Patent Application Publication No. 2015-16273 describes the Lyapunov exponent calculation, but does not describe the user test results related to the Lyapunov exponent and the drowsiness level.

As described above, the above known examples are different from the present disclosure in terms of the relationship between the Lyapunov exponent and the brain activity and furthermore the drowsiness level.

103 The analysis unitoutputs the drowsiness level information showing that the drowsiness level decreases as the index value increases, and that the drowsiness level increases as the index value decreases. The drowsiness level information is information showing a current drowsiness level, time-series drowsiness levels, or both of them.

Thus, the user can grasp the drowsiness level based on the drowsiness level information.

1 1 The drowsiness level estimation deviceof Embodiment 1 estimates the brain activity level of the central nerve by analyzing the pulse waveform fluctuation, and estimates the drowsiness level based only on the brain activity level. A drowsiness level estimation deviceof Embodiment 2 estimates an autonomic activity level based on a pulse interval fluctuation in addition to the brain activity level, and estimates the drowsiness level based on both the central nerve activity and the autonomic activity. The differences of Embodiment 2 from Embodiment 1 are mainly described below, and the configuration that is not described in Embodiment 2 is similar to that in Embodiment 1.

12 FIG. 1 1 10 is a block diagram illustrating the configuration of the drowsiness level estimation deviceaccording to Embodiment 2 and the use configuration of the drowsiness level estimation device. The Doppler sensordetects the pulse of the user in addition to the pulse wave of the user.

The pulse interval is generally called “R-R Interval (RRI).” The RRI is subjected to frequency conversion into various types of information such as an autonomic balance described later. In vital analysis in which a pulse, blood pressure, and respiration are analyzed, an infinitesimal fluctuation having an extremely low frequency characteristic of about 1 Hz is analyzed unlike body movement analysis or other analysis. Therefore, in the vital analysis, analog detection using a 24-GHz Doppler system is preferable to 60 to 79 GHz that is often used for distance measurement because of high resolution.

10 The Doppler sensordetects the autonomic balance from a shift of the pulse interval of the user (one-dimensional pattern pulse shift). The autonomic balance is a balance between the sympathetic nerve and the parasympathetic nerve. The autonomic balance is the ratio between a low frequency (LF) and a high frequency (HF), and is calculated by LF/HF. LF shows a sympathetic nerve activity, and HF shows a parasympathetic nerve activity. The sympathetic nerve is dominant during the daytime or in an active state, and the parasympathetic nerve is dominant during the nighttime or in a calm state.

LF is obtained by an integrated value of powers in a low frequency band of, for example, 0.05 Hz to 0.15 Hz on a characteristic curve. HF is obtained by an integrated value of powers in a high frequency band of, for example, 0.15 Hz to 0.40 Hz on the characteristic curve. The characteristic curve is obtained by frequency expansion on time-series pulse intervals, and is plotted on coordinate axes including the horizontal axis representing frequency and the vertical axis representing power.

Since the autonomic balance is LF/HF, when LF is relatively large, the sympathetic nerve is dominant, and an excited or active state can be estimated. When LF is relatively small, the parasympathetic nerve is dominant, and a relaxed state can be estimated. When the numerical value of the autonomic balance is large, the excited state can be estimated. When the numerical value of the autonomic balance is small, the relaxed or comfortable state can be estimated. When the human body is excited or active, the activity level in the autonomic nervous system is high, and the value of the autonomic balance is large. When the human body is relaxed, the activity level in the autonomic nervous system is low, and the value of the autonomic balance is small. Thus, the autonomic balance can be used as an index showing the autonomic activity level.

The correspondence relationship among the autonomic balance, the autonomic activity level, and the state of the user is as follows. That is, when the numerical value of the autonomic balance is small, the autonomic activity level is low, and the user is relaxed or comfortable. When the numerical value of the autonomic balance is large, the autonomic activity level is high, and the user is excited or active.

10 10 10 10 In this manner, the Doppler sensorcan detect the autonomic activity level based on the autonomic balance. As described above, the Doppler sensorcan detect the brain activity level in the central nervous system based on the pulse wave. That is, the Doppler sensoralone can detect the autonomic activity and the central nerve activity. The pulse that can be measured by the Doppler sensorrefers to a pulse rate or a pulsing motion, and includes the magnitude of the pulse rate or a time-series increase or decrease in the pulse rate, the magnitude of the pulse interval or a time-series increase or decrease in the pulse interval, the magnitude of LF or a time-series increase or decrease in LF, and the magnitude of LF/HF or a time-series increase or decrease in LF/HF.

1 The drowsiness level estimation deviceof Embodiment 2 estimates the drowsiness level from both the central nerve activity and the autonomic activity. The concept of drowsiness level estimation from both the central nerve activity and the autonomic activity is described below.

First, the inventors have found that the autonomic activity level is related to the accuracy of the drowsiness level. The inventors have conducted experiment in which, during a predetermined time, the pulse of the user is measured and subjective reports are received from the user as to whether the user is drowsy or not. As a result, the inventors have found that the pulse rate of a plurality of users who is drowsy is lower by 4 beats per minute on average than the pulse rate of the plurality of users who is not drowsy. Thus, the pulse rate affects the drowsiness level estimation.

Although the drowsiness level can be estimated only by the brain activity level in the central nervous system as described above, the inventors have found that the estimation accuracy can be increased when the drowsiness level is estimated by the autonomic activity level in combination. Although the drowsiness level increases as the brain activity level decreases, the inventors have found through a test that, when the autonomic activity level is lower while the brain activity level is low, the user is relaxed and the drowsiness level is higher. Although the drowsiness level decreases as the brain activity level increases, the inventors have found through a test that, when the autonomic activity level is higher while the brain activity level is high, the user is excited or active and the drowsiness level is lower.

The inventors have found through the tests that the accuracy is improved for the result of estimation that the drowsiness level increases as the autonomic activity level decreases, and that the accuracy is improved for the result of estimation that the drowsiness level decreases as the autonomic activity level increases. That is, the inventors have found a new advantageous effect in that the accuracy is improved for both the high-drowsiness state and the low-drowsiness state by the combination of the two factors that are the brain activity level in the central nervous system and the autonomic activity level.

Using a simple example, description is made about the fact that the drowsiness level is related not only to the brain activity level in the central nervous system but also to the autonomic activity level. While the human autonomic nerve is working all day long, the human is highly relaxed or comfortable when he/she has drowsiness, in particular, falls into a doze. Therefore, it is considered that the drowsiness level can be estimated with higher accuracy when the autonomic activity as well as the brain activity in the central nervous system is evaluated. Thus, there is an advantageous effect in that the drowsiness level can be estimated with high accuracy using both the brain activity in the central nervous system and the autonomic activity.

The drowsiness level is affected by the central nerve activity more greatly than the autonomic activity. In other words, the brain activity that is the central nerve activity has a higher contribution factor to the drowsiness level than the autonomic activity. Therefore, in the drowsiness level estimation, the brain activity level may be used with a contribution factor of at least 50% or more and 90% or less. The brain activity level is preferably used with a contribution factor of 60% or more and 80% or less. When the contribution factor of the brain activity level is outside the above range, the accuracy of the estimated drowsiness level may decrease. Thus, the contribution factor of the brain activity level is preferably 60% or more and 80% or less.

In the drowsiness level estimation, the autonomic activity level may be used with a lower contribution factor than that of the brain activity level. The autonomic activity level is preferably used with a contribution factor of 20% or more and 40% or less. When the contribution factor of the autonomic activity level is outside the above range, the accuracy of the estimated drowsiness level may decrease. Thus, the contribution factor of the autonomic activity is preferably 20% or more and 40% or less. The contribution factor of the brain activity level to the drowsiness level is hereinafter referred to as “first contribution factor.” The contribution factor of the autonomic activity level to the drowsiness level is hereinafter referred to as “second contribution factor.” The first contribution factor shows how greatly the brain activity level affects the drowsiness level. The second contribution factor shows how greatly the autonomic activity level affects the drowsiness level.

An example of the drowsiness level estimation using the contribution factors is described below. When the first contribution factor is 70% and the second contribution factor is 30%, the drowsiness level is calculated by “drowsiness level=0.7×brain activity level+0.3×autonomic activity level.” For example, it is assumed that the drowsiness level is expressed by a grade value of 1 to 100 and the drowsiness level increases as the value increases. It is also assumed that the brain activity level and the autonomic activity level are expressed by grade values of 1 to 100. Since the numerical value of the drowsiness level increases when the drowsiness level increases, larger numerical values are assigned to the brain activity level and the autonomic activity level for use in the above calculation formula as the drowsiness level increases. That is, the brain activity level has a larger value as the brain activity level decreases, and the autonomic activity level has a larger value as the autonomic activity level decreases.

It should be noted that the magnitude relationship of the numerical value of the autonomic activity level for use in the above calculation formula is opposite to the magnitude relationship of the autonomic activity level based on LF/HF showing the autonomic balance. That is, the autonomic activity level based on LF/HF has a larger value as the autonomic activity level increases, and the magnitude relationship is opposite to that of the numerical value of the autonomic activity level for use in the above calculation formula. The magnitude relationship of the numerical value of the autonomic activity level for use in the above calculation formula is used only in the drowsiness level calculation using the contribution factors. In the following description, the autonomic activity level has the magnitude relationship based on LF/HF.

When the brain activity level and the autonomic activity level are expressed by the above grade values, the brain activity level is 100, and the autonomic activity level is 100, the drowsiness level is the maximum value of 100 based on the above formula. When the brain activity level is 80 and the autonomic activity level is 50, the drowsiness level is 71.

12 FIG. The concept of drowsiness level estimation using both the brain activity level and the autonomic activity level has been described clearly above. The description now returns to.

1 10 103 10 103 103 103 The drowsiness level estimation devicedetects the pulse wave and the pulse of the user with the Doppler sensor. The analysis unitestimates the brain activity level in the central nervous system by analyzing the pulse wave detected by the Doppler sensor, and estimates the activity level in the autonomic nervous system from the pulse. The analysis unithas the first contribution factor and the second contribution factor. The first contribution factor is larger than the second contribution factor. The second contribution factor is smaller than the first contribution factor. As described above, the first contribution factor is preferably 60% or more and 80% or less, and the second contribution factor is preferably 20% or more and 40% or less. The analysis unitestimates the drowsiness level in the manner described above based on the brain activity level, the autonomic activity level, the first contribution factor, and the second contribution factor. Then, the analysis unitoutputs drowsiness level information showing the estimated drowsiness level.

103 107 106 107 1 103 103 108 106 103 107 106 103 104 1 The drowsiness level information output from the analysis unitis input to the display unitvia the cloud unitand displayed on the display unit. Since the drowsiness level estimation devicevisualizes the drowsiness level information by outputting it from the analysis unit, the user can grasp the drowsiness level. The drowsiness level information output from the analysis unitmay be accumulated in the data collection unitvia the cloud unit. The drowsiness level information output from the analysis unitmay directly be input to and displayed on the display unitwithout intermediation of the cloud unit. The drowsiness level information output from the analysis unitmay be input to the control detail determination unitand used to determine details of control on the apparatus including the drowsiness level estimation device.

The human autonomic nerve is working all day long. During work or study mainly in the daytime, the sympathetic nerve in the autonomic nervous system called “good stress” is highly active. When the sympathetic nerve in the autonomic nervous system is highly active, the human excites the brain, dilates the trachea, increases the heart rate, constricts the blood vessels, increases the blood pressure, suppresses gastrointestinal motility, and promotes sweating. Thus, the highly active sympathetic nerve in the autonomic nervous system is good for work and study. That is, when the sympathetic nerve of LF is highly active or the ratio expressed by LF/HF about the autonomic balance is high, the user is in a good condition for work and study. When “drowsiness level is low” and “excited or active emotion is high,” the user can make progress.

103 103 That is, when the drowsiness level is low and the ratio about the autonomic balance is high, evaluation can be made that the user is in an optimum condition for work and study, in other words, the user's work efficiency is high. Thus, the analysis unitmay output work efficiency information showing that the user's work efficiency is high when the brain activity level is lower than a preset first threshold and the autonomic activity level is higher than a preset second threshold. In this manner, the analysis unitmay output not only the drowsiness level but also other information determined from the brain activity level and the autonomic activity level.

When the sympathetic nerve in the autonomic nervous system is less active, the human calms the brain, narrows the trachea, reduces the heart rate, dilates the blood vessels, reduces the blood pressure, activates gastrointestinal motility, and suppresses sweating. Thus, the less active sympathetic nerve in the autonomic nervous system leads to relaxation. That is, when the sympathetic nerve of LF is less active or the ratio expressed by LF/HF about the autonomic balance is low, the user is relaxed or comfortable. When “drowsiness level is high” and “excited or active emotion is low,” the user is relaxed.

103 103 That is, when the drowsiness level is high and the ratio about the autonomic balance is low, evaluation can be made that the user is most relaxed or comfortable, in other words, in a good condition for rest. Thus, the analysis unitmay output relaxation level information showing that the user's relaxation level is high when the brain activity level is higher than a preset third threshold and the autonomic activity level is lower than a preset fourth threshold. In this manner, the analysis unitmay output not only the drowsiness level but also other information determined from the brain activity level and the autonomic activity level.

1 10 1 1 10 1 As described above, the drowsiness level estimation deviceof Embodiment 2 can obtain the advantageous effects similar to those of Embodiment 1, and estimate the autonomic activity level by detecting the pulse with the Doppler sensor. Therefore, the drowsiness level estimation devicecan estimate the autonomic activity level in addition to the brain activity level in the central nervous system. The drowsiness level estimation devicecan estimate the drowsiness level with high accuracy from the brain activity level in the central nervous system, the autonomic activity level, the first contribution factor, and the second contribution factor. Since the central nerve activity and the autonomic activity can be measured by the single Doppler sensor, the drowsiness level estimation devicecan estimate emotions remotely in a short time without the need for other devices compared with a device using an electroencephalograph or an imaging unit.

1 1 The drowsiness level estimation devicesof Embodiments 1 and 2 each estimate the drowsiness level of the user at a certain time point from the single Lyapunov exponent. A drowsiness level estimation deviceof Embodiment 3 estimates an average drowsiness level of the user and a change in the drowsiness level in a predetermined section. The differences of Embodiment 3 from Embodiments 1 and 2 are mainly described below, and the configuration that is not described in Embodiment 3 is similar to those in Embodiments 1 and 2.

1 1 103 1 1 103 1 1 103 The drowsiness level estimation deviceof Embodiment 3 is similar to the drowsiness level estimation devicesof Embodiments 1 and 2 in that the analysis unitgenerates the Lyapunov exponent that is the index value by digitizing the pulse wave based on the chaos analysis in which the source of analysis is the pulse waveform shift that is the time-series shift of the waveform of the pulse wave. The drowsiness level estimation deviceof Embodiment 3 estimates a drowsiness level of the user at one certain time point (hereinafter referred to as “single-point drowsiness level”) based on the Lyapunov exponent. In the drowsiness level estimation deviceof Embodiment 3, the analysis unitfurther estimates an average drowsiness level and a change in the drowsiness level in a predetermined section. The drowsiness level estimation deviceof Embodiment 3 is different from the drowsiness level estimation devicesof Embodiments 1 and 2 in that the analysis unitestimates the average drowsiness level and the change in the drowsiness level in the predetermined section.

1 1 1 As described above, the drowsiness level estimation devicecan calculate the Lyapunov exponent, estimate the drowsiness level, and output the drowsiness level information at a short time interval of, for example, 1 minute. When the drowsiness level estimation deviceis provided to an air-conditioning apparatus or other apparatuses and the apparatus changes apparatus control based on the drowsiness level information output from the drowsiness level estimation deviceat the short time interval, the following problem arises. For example, when the apparatus is an air-conditioning apparatus, the drowsiness level varies at the short time intervals, and the variation in the drowsiness level is reflected directly in, for example, air-conditioning control or alert on a display unit, the change in the air-conditioning control or the alert on the display unit are frequently performed in a short time, and the user may be dissatisfied.

1 1 1 1 In view of the control to be performed by the apparatus including the drowsiness level estimation device, the drowsiness level estimation devicecalculates and outputs, in addition to the single-point drowsiness level, a first-order short section drowsiness level B obtained by averaging time-series data of the single-point drowsiness levels, and a second-order short section drowsiness level C obtained by averaging time-series data of the first-order short section drowsiness levels B. That is, the drowsiness level estimation devicecalculates an average drowsiness level of the user in a predetermined section, and outputs drowsiness level information for each predetermined section. Thus, the apparatus including the drowsiness level estimation deviceperforms the apparatus control at an appropriate frequency, and the number of times of unnecessary apparatus control can be reduced.

1 1 1 The drowsiness level estimation devicecan digitize the drowsiness level at a certain time point or in a predetermined section. Since the magnitude of the numerical value differs depending on users, estimation as to whether the drowsiness level increases or decreases is further required in view of versatility of an apparatus for a large number of unspecified users. When the increase or decrease in the drowsiness level is determined, the apparatus including the drowsiness level estimation devicecan perform appropriate apparatus control and issue an alert at an appropriate timing using an application screen, sound, or light based on the increase or decrease in the drowsiness level. Therefore, the drowsiness level estimation deviceof Embodiment 3 can also estimate a change in the drowsiness level as detailed below.

1 1 103 A specific configuration is described below. The drowsiness level estimation deviceof Embodiment 3 is different from the drowsiness level estimation devicesof Embodiments 1 and 2 in terms of the configuration of the analysis unit.

13 FIG. 13 14 FIGS.and 103 1 103 103 103 103 103 103 103 103 103 103 a g d e f g b c is a block diagram of the analysis unitof the drowsiness level estimation deviceaccording to Embodiment 3. The analysis unitincludes a single-point drowsiness level calculation unit, a short section drowsiness level calculation unit, a long section drowsiness level calculation unit, a drowsiness level change estimation unit, and a drowsiness level information output unit. The short section drowsiness level calculation unitcalculates a short section drowsiness level by performing an averaging process on time-series data of the single-point drowsiness levels in a short section, and includes a first-order calculation unitthat calculates the first-order short section drowsiness level B, and a second-order calculation unitthat calculates the second-order short section drowsiness level C. Those calculation units are each functionally configured by a CPU of the analysis unitand a control program. Operations of the calculation units, the first-order short section drowsiness level B, the second-order short section drowsiness level C, and a long section drowsiness level D are described below with reference to.

14 FIG. 1 103 103 103 103 a a a a is a conceptual diagram of the first-order short section drowsiness level B, the second-order short section drowsiness level C, and the long section drowsiness level D calculated by the drowsiness level estimation deviceaccording to Embodiment 3. The single-point drowsiness level calculation unitcalculates a Lyapunov exponent by the process including the first to third steps described in Embodiment 1, estimates a single-point drowsiness level A based on the Lyapunov exponent, and outputs it. Specifically, the single-point drowsiness level calculation unitfirst generates an attractor based on vectors X(0) to X(n), and calculates a Lyapunov exponent based on trajectories of the attractor. Then, the single-point drowsiness level calculation unitcalculates a single-point drowsiness level A by indexation of a drowsiness level at one time point based on the calculated Lyapunov exponent, and outputs it. The Lyapunov exponent used to calculate the single-point drowsiness level A may be a raw value of the Lyapunov exponent, or may be a value obtained by normalizing the raw value of the Lyapunov exponent by percentage or grades. In any case, the single-point drowsiness level calculation unitcalculates the single-point drowsiness level A at one time point based on the Lyapunov exponent and outputs it.

1 103 1 103 a a In one example, when a time Tthat is the output window length is 60 seconds and a sampling time interval is 1 second, the single-point drowsiness level calculation unitgenerates an attractor from 60 pieces of time-series data X(0) to X(60) obtained in 60 seconds, and outputs one single-point drowsiness level A after an elapse of 60 seconds from the start of measurement. In another example, when the time Tis 60 seconds and the sampling time interval is 2 seconds, the single-point drowsiness level calculation unitgenerates an attractor from 30 pieces of time-series data obtained in 60 seconds, and outputs one single-point drowsiness level A after an elapse of 60 seconds from the start of measurement.

1 1 1 1 1 1 The time Tand the sampling time interval can be selected as appropriate depending on the accuracy, calculation time, and memory size. Since the human drowsiness level changes even in about 1 minute, however, the time Tis optimally 1 minute or less. When the time Tis conversely a long time such as 5 minutes, the drowsiness level increases, decreases, or changes in different directions during measurement, and the attractor cannot be generated accurately. Thus, the long time Tis inappropriate. When the time Tis a long time such as 5 minutes, there is a disadvantage in that the single-point drowsiness level A is smoothed during the long time and the midway drowsiness level change is hardly observed. This leads to a decrease in the estimation accuracy. Thus, the time Tis preferably less than 5 minutes.

103 10 103 103 a a The single-point drowsiness level calculation unitrepeats the process of outputting the single-point drowsiness level A during the measurement using the Doppler sensor. The single-point drowsiness level calculation unituses time-series vector data of the vectors X used to calculate the single-point drowsiness level A while shifting the time-series vector data by the sampling time interval each time. The description is made below under the assumption that the sampling time interval of each calculation unit of the analysis unitis 1 second.

1 103 103 103 103 103 a a a a a A specific example in which the time Tis 60 seconds is described. In this case, the single-point drowsiness level calculation unitgenerates an attractor based on 60 pieces of time-series vector data X(1) to X(60) obtained in 60 seconds from the start of measurement, calculates a single-point drowsiness level A(1) firstly, and outputs it. To obtain a single-point drowsiness level A(2) secondly, the single-point drowsiness level calculation unituses time-series vector data X(2) to X(61) by shifting the time-series vector data. That is, the single-point drowsiness level calculation unitgenerates an attractor based on the time-series vector data X(2) to X(61), calculates the single-point drowsiness level A(2), and outputs it. Similarly, the single-point drowsiness level calculation unitgenerates an attractor based on time-series vector data X(3) to X(62), and outputs a single-point drowsiness level A(3). After the single-point drowsiness level A(1) is output firstly, the single-point drowsiness level calculation unitrepeats the process of outputting the single-point drowsiness level A every 1 second until the end of measurement.

103 103 103 103 2 103 2 103 103 b a b b b b b 14 FIG. The first-order calculation unitacquires time-series data of the single-point drowsiness levels A output from the single-point drowsiness level calculation unit. The first-order calculation unitcalculates a first-order short section drowsiness level B by performing an averaging process such as a moving average process on the time-series data of the single-point drowsiness levels A acquired in a short section. In, the short section for the first-order calculation unitis a time T. That is, the first-order calculation unitcalculates the first-order short section drowsiness level B by averaging the plurality of single-point drowsiness levels A within the time T. The first-order calculation unitoutputs the calculated first-order short section drowsiness level B. The first-order calculation unituses the time-series data of the single-point drowsiness levels A used to calculate the first-order short section drowsiness level B while shifting the time-series data by the sampling time interval each time.

2 103 103 103 103 14 FIG. b b b b A specific example in which the time Tis 90 seconds, that is, “n” inis 90 is described. In this case, the first-order calculation unitcalculates a first-order short section drowsiness level B(1) by performing the averaging process on 90 pieces of time-series data A(1), . . . , A(90), and outputs it. To calculate a first-order short section drowsiness level B(2) secondly, the first-order calculation unituses time-series data A(2), . . . , A(91) by shifting the time-series data. That is, the first-order calculation unitcalculates the first-order short section drowsiness level B(2) using the time-series data A(2), . . . , A(91), and outputs it. After the first-order short section drowsiness level B(1) is output firstly, the first-order calculation unitrepeats the process of outputting the first-order short section drowsiness level B every 1 second until the end of measurement.

1 2 1 2 2 The first-order short section drowsiness level B is obtained by the averaging process on the time-series data of the single-point drowsiness levels A, and is therefore data that smooths out up-and-down numerical value variations of the single-point drowsiness levels A. That is, the first-order short section drowsiness level B is an index value with which an accurate drowsiness level can be grasped even if the time-series data of the single-point drowsiness levels A has up-and-down variations. By using the first-order short section drowsiness level B as the drowsiness level to perform apparatus control based on the drowsiness level, the apparatus including the drowsiness level estimation devicecan perform more stable control than in the case where the single-point drowsiness level A is used. Since an increase in the human drowsiness level is observed even in about 1 minute, the time Tis optimally 1 minute or less similarly to the time T. When the time Tis set extremely long, the drowsiness level is excessively smoothed and the midway drowsiness level change may be undetected. Thus, the time Tis preferably 3 minutes or less.

Various processes such as arithmetical averaging, weighted averaging, geometrical averaging, or harmonic averaging are used as the averaging process. Since the drowsiness level has a time-series tendency, the moving average process is suitable as the averaging process.

2 Time-series data obtained by calculating the first-order short section drowsiness levels B during a predetermined period is represented by a smooth curve in a graph having a vertical axis representing the first-order short section drowsiness level B, and a drowsiness level that reflects the tendency of the drowsiness level change in each section during the time Tis finally obtained. This method is effective in a frequently varying event typified by the brain activity or the pulse wave, and in removal of noise superimposed on the input signal.

1 2 To further reduce the calculation time or the calculation amount in the drowsiness level estimation, the drowsiness level estimation devicepreferably uses simple averaging. For example, when the time Tis 90 seconds, the first-order short section drowsiness level B may be a value obtained by simply averaging the 90 single-point drowsiness levels A, namely A(1), . . . , A(90). In this case, there is an advantage in that the calculation amount is reduced though the accuracy decreases.

103 103 103 103 3 103 3 103 103 c b c c c c c 14 FIG. The second-order calculation unitacquires time-series data of the first-order short section drowsiness levels B output from the first-order calculation unit. The second-order calculation unitcalculates a second-order short section drowsiness level C by further performing the averaging process on the time-series data of the first-order short section drowsiness levels B acquired in a short section, and outputs it. In, the short section for the second-order calculation unitis a time T. That is, the second-order calculation unitcalculates the second-order short section drowsiness level C by averaging the plurality of first-order short section drowsiness levels B within the time T. The second-order calculation unitoutputs the calculated second-order short section drowsiness level C. The second-order calculation unituses the time-series data of the first-order short section drowsiness levels B used to calculate the second-order short section drowsiness level C while shifting the time-series data by the sampling time interval each time.

3 103 103 103 103 14 FIG. c c c c A specific example in which the time Tis 60 seconds, that is, “m” inis 60 is described. In this case, the second-order calculation unitcalculates a second-order short section drowsiness level C(1) by performing the averaging process on 60 pieces of time-series data B(1), . . . , B(60), and outputs it. To calculate a second-order short section drowsiness level C(2) secondly, the second-order calculation unituses time-series data B(2), . . . , B(61) by shifting the time-series data. That is, the second-order calculation unitcalculates the second-order short section drowsiness level C(2) using the time-series data B(2), . . . , B(61), and outputs it. After the second-order short section drowsiness level C(1) is output firstly, the second-order calculation unitrepeats the process of outputting the second-order short section drowsiness level C every 1 second until the end of measurement.

103 103 103 103 103 a b c f f The single-point drowsiness level A output from the single-point drowsiness level calculation unit, the first-order short section drowsiness level B output from the first-order calculation unit, and the second-order short section drowsiness level C output from the second-order calculation unitare input to the drowsiness level information output unit. The drowsiness level information output unitmay output any one of the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C as the drowsiness level information, or may output part or all of them.

103 1 f The second-order short section drowsiness level C is an index value with a stable numerical value or a stable numerical value tendency by further smoothing compared with the first-order short section drowsiness level B. In the second-order short section drowsiness level C, the midway fine drowsiness level increase is hardly observed. Therefore, when outputting any one of the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C, the drowsiness level information output unitmay make selection depending on the frequency, response, and estimated time of control change to be performed by the apparatus including the drowsiness level estimation device.

103 103 2 1 1 2 103 3 3 1 103 103 To reduce the calculation time and the calculation amount while increasing the accuracy, the analysis unitmay perform the following process. The analysis unitfirst sets the time Tto be sufficiently shorter than the time T, and calculates the first-order short section drowsiness level B by performing a moving average process on the time-series data of the single-point drowsiness levels A within the short time. Thus, the drowsiness level estimation accuracy can be increased compared with the simple averaging. For example, Tis set to 30 seconds, and Tis set to 10 seconds to 15 seconds. Then, the analysis unitcalculates the second-order short section drowsiness level C by performing the averaging process using simple averaging on the time-series data of the first-order short section drowsiness levels B within the time T. When Tis 30 seconds, the drowsiness level estimation devicecalculates the second-order short section drowsiness level C by simply averaging 30 first-order short section drowsiness levels B. With the simple averaging, the analysis unithas an advantage in that the calculation amount is reduced though the accuracy decreases. Through the above process, the analysis unitcan reduce the calculation time and the calculation amount while increasing the accuracy.

103 103 4 4 2 d d 14 FIG. The long section drowsiness level calculation unitcalculates a long section drowsiness level D. The long section drowsiness level calculation unitcalculates the long section drowsiness level D by a method similar to that for the short section drowsiness level using time-series data of the single-point drowsiness levels A or the short section drowsiness levels acquired in a long section that is longer than the above short sections. In, the long section is a time T. The time Tis set longer than the time Tthat is the acquisition time for the time-series data used for the calculation of the first-order short section drowsiness level B.

103 4 103 103 103 d d d d The long section drowsiness level calculation unitcalculates the long section drowsiness level D by performing an averaging process such as a moving average process on the time-series data of the single-point drowsiness levels A within the time T. The long section drowsiness level calculation unitmay calculate the long section drowsiness level D by performing the averaging process on the time-series data of the short section drowsiness levels. When the short section drowsiness levels are used for the calculation of the long section drowsiness level D, the long section drowsiness level calculation unitmay use either of the first-order short section drowsiness levels B and the second-order short section drowsiness levels C. When the short section drowsiness levels are used in the case where the moving average process is performed as the averaging process, the long section drowsiness level calculation unitcan reduce the moving average process time compared with the case where the single-point drowsiness levels A are used, thereby reducing the storage area or the calculation time. The averaging process may be the moving average process, or may be a simple averaging process on a plurality of past short section drowsiness levels when its count is small.

103 e The long section drowsiness level D is defined as an index value showing a normal drowsiness level of the user. The long section drowsiness level D is used for estimation of a change in the drowsiness level by the drowsiness level change estimation unitdescribed later. Estimation as to whether the drowsiness level changes requires a current drowsiness level at the time of estimation and a drowsiness level for comparison. The long section drowsiness level D is used as a target of the comparison. The first-order short section drowsiness level B or the second-order short section drowsiness level C is used as the current drowsiness level at the time of estimation.

4 2 2 4 4 2 4 2 4 4 As described above, the time Tis set longer than the time T. For example, the time Tis about 1 minute, and is about 5 minutes at the maximum. For example, the time Tis preferably a value from 3 minutes to 60 minutes. When the time Tis shorter than 3 minutes, there is no temporal difference from the time T, and the long section drowsiness level D cannot be differentiated from the first-order short section drowsiness level B. Thus, the comparison is difficult. When the time Tis equal to or longer than a triple of the time T, the long section drowsiness level D can be differentiated from the first-order short section drowsiness level B. Thus, the comparison is easy. When the time Tis longer than 60 minutes, however, the calculation result of the long section drowsiness level D may include a behavior or state of the user different from usual, and the long section drowsiness level D can hardly be regarded as the index value showing the normal drowsiness level of the user. In this case, the accuracy of the result of estimation of the drowsiness level change may decrease. Therefore, the time Tis preferably 60 minutes at the maximum.

103 103 103 103 e e e e The drowsiness level change estimation unitestimates a change in the drowsiness level of the user. The drowsiness level change estimation unitestimates an increase or decrease in the drowsiness level as the change in the drowsiness level. The drowsiness level change estimation unitestimates the degree of the change in the drowsiness level as the change in the drowsiness level. The drowsiness level change estimation unitcan estimate both the increase and the decrease in the drowsiness level. Since the concept of estimation is basically the same, the following description is directed to the estimation of the increase in the drowsiness level.

103 103 103 103 e g d e The drowsiness level change estimation unitestimates whether the drowsiness level increases based on the short section drowsiness level calculated by the short section drowsiness level calculation unitand the long section drowsiness level D calculated by the long section drowsiness level calculation unit. The drowsiness level change estimation unitmay use either of the first-order short section drowsiness level B and the second-order short section drowsiness level C as the short section drowsiness level. The description is continued under the assumption that the first-order short section drowsiness level B is used.

103 103 103 103 103 103 e b e e e d The drowsiness level change estimation unitestimates the increase in the drowsiness level using a current first-order short section drowsiness level B at the time of estimation and a past long section drowsiness level D. The current first-order short section drowsiness level B at the time of estimation refers to a real-time (including latest) first-order short section drowsiness level B input from the first-order calculation unitto the drowsiness level change estimation unitat the time of estimation. The drowsiness level change estimation unituses a long section drowsiness level D in the past time frame as the long section drowsiness level D. Since the long section drowsiness level D is used as a target of comparison with the current drowsiness level, the long section drowsiness level D is preferably the one calculated from measurement data in a time frame in which the drowsiness level does not relatively increase. Thus, the drowsiness level change estimation unituses a long section drowsiness level D in a time frame in which the drowsiness level is lowest or relatively low in the time-series data of the long section drowsiness levels D that are calculated by the long section drowsiness level calculation unitand older than the current long section drowsiness level D at the time of estimation.

103 103 103 4 e e e a 14 FIG. The drowsiness level change estimation unitdesirably uses, as the long section drowsiness level D, a long section drowsiness level D calculated using time-series data of past single-point drowsiness levels A that do not include the single-point drowsiness levels A used for calculation of the current first-order short section drowsiness level B at the time of estimation. In the example of, when the drowsiness level change estimation unitestimates the increase in the drowsiness level at the timing at which the first-order short section drowsiness level B(1) is output, the drowsiness level change estimation unitdesirably estimates the increase in the drowsiness level using the first-order short section drowsiness level B(1) and a long section drowsiness level Da. The long section drowsiness level Da is calculated using time-series data of single-point drowsiness levels A within a past time Tthat do not include the single-point drowsiness levels A(1), . . . , A(n) used for the calculation of the first-order short section drowsiness level B(1).

If the long section drowsiness level D to be used for the estimation of the increase in the drowsiness level is calculated by including the single-point drowsiness levels A(1), . . . , A(n) used for the calculation of the first-order short section drowsiness level B(1), the calculated long section drowsiness level D is affected by the drowsiness level at the current time point. Therefore, the long section drowsiness level D is desirably calculated using the time-series data of the past single-point drowsiness levels A that do not include the single-point drowsiness levels A(1), . . . , A(n) used for the calculation of the first-order short section drowsiness level B(1). Although the accuracy decreases, the long section drowsiness level D may be calculated by including the single-point drowsiness levels A(1), . . . , A(n).

Although the description has been made about the example in which the long section drowsiness level D is calculated based on the single-point drowsiness levels A, the long section drowsiness level D may be calculated based on the short section drowsiness levels such as the first-order short section drowsiness levels B or the second-order short section drowsiness levels C as described above. When the long section drowsiness level D is calculated based on the short section drowsiness levels, the long section drowsiness level D can be calculated with high accuracy without including the current short section drowsiness level at the time of estimation, but may be calculated by including the current short section drowsiness level at the time of estimation.

Based on the result of observation of the working user, the inventors have considered that, to arouse the user with an increasing drowsiness level, it is optimum to supply an airflow or propose a rest once or twice every 15 to 60 minutes and repeat this operation. Therefore, the long section drowsiness level D to be used for the estimation of the change in the drowsiness level is a past long section drowsiness level D that was calculated during measurement on the same day as the day of the estimation and does not include the current short section drowsiness level at the time of estimation. In one example, when the current short section drowsiness level at the time of estimation is based on measurement data within 1 minute, the long section drowsiness level D is calculated based on measurement data in the past time that does not include the 1 minute, preferably measurement data within, for example, 3 to 30 minutes immediately before the 1 minute.

103 103 103 e e e The drowsiness level change estimation unitrepeatedly estimates and outputs the change in the drowsiness level at any appropriate timing. The following value may be used as the long section drowsiness level D to be used for second estimation onward. The drowsiness level change estimation unitmay use, as the long section drowsiness level D to be used for the second estimation onward, the long section drowsiness level D used in the previous estimation that the drowsiness level does not increase. In this case, there is an advantageous effect in that the drowsiness level change estimation unitcan be prevented from erroneously estimating that the drowsiness level does not increase though the drowsiness level increases in actuality.

A method for estimating the increase in the drowsiness level using the first-order short section drowsiness level B and the long section drowsiness level D is described below. In this estimation, a drowsiness level increase index value is used. This is expressed by a multiple of the first-order short section drowsiness level B relative to the long section drowsiness level D. The drowsiness level increase index value is calculated by “short section drowsiness level/long section drowsiness level.” When the drowsiness level is, for example, expressed in 100 levels from 1 to 100 and the value increases as the drowsiness level increases, the drowsiness level increase index value may range from 0 to 100. The drowsiness level increase index value is 1.0 when the long section drowsiness level D is equal to the first-order short section drowsiness level B and the drowsiness level does not change, and is away from 1.0 as the degree of the increase in the drowsiness level increases. In the actual measurement during normal work or study, the first-order short section drowsiness level B may have a value that is approximately a half to a double of the long section drowsiness level D serving as the reference, and the drowsiness level increase index value may range from 0.5 to 2.0.

103 e When the drowsiness level increase index value is equal to or larger than a threshold, the drowsiness level change estimation unitestimates that the drowsiness level increases. For example, the threshold is optimally a value of 1.1 to 1.7. When the threshold is larger than 1.7, there is an increasing risk that estimation cannot be made that the drowsiness level increases unless the drowsiness level increases considerably. Although the threshold may be increased so that the apparatus control frequency is reduced, the threshold is preferably 1.7 or less for at least the drowsiness level increase estimation.

1 1 1 When the threshold is 1.1 or more, the apparatus including the drowsiness level estimation devicecan perform apparatus control to arouse the user under the estimation that the drowsiness level increases at a stage at which the drowsiness level does not increase so greatly. Therefore, the current drowsiness level can be maintained. The drowsiness level estimation deviceuses the method in which the drowsiness level estimation accuracy is increased by indexation of the brain activity level. When the threshold is smaller than 1.1, however, the drowsiness level estimation devicemay erroneously estimate that the drowsiness level increases at a state at which the drowsiness level does not increase in actuality. Therefore, the threshold is 1.1 at the minimum. When the threshold is a value of 1.2 to 1.5, it is possible to avoid the risk that the result of estimation that the drowsiness level increases can be obtained only when the drowsiness level increases greatly, and to avoid the erroneous estimation.

As described above, the drowsiness level increase index value is 1.0 when the long section drowsiness level D is equal to the first-order short section drowsiness level B, and is larger than 1.0 when the drowsiness level increases. In a specific example, the drowsiness level is expressed in 100 levels from 1% to 100%. For example, when the long section drowsiness level D serving as the reference is 40% and the first-order short section drowsiness level B is 45%, the multiple is 1.125. When the first-order short section drowsiness level B is 70%, the multiple is 1.75. In this case, the threshold for the estimation that the drowsiness level increases is a value close to 1.1 when the drowsiness level increase is to be detected quickly to increase the control frequency. That is, an increase of 5% or more from the reference of 40% is detected and determination is made that the drowsiness level increases.

The threshold for the estimation that the drowsiness level increases is a value close to 1.7 when the detection is to be made after the drowsiness level increases greatly to reduce the control frequency. That is, when the drowsiness level increase index value increases by 30% or more from the reference of 40%, determination is made that the drowsiness level increases. When the drowsiness level is expressed in 10 levels from 1 to 10, the drowsiness level increase index value may have a value of 0 to 10 by calculation. For example, when the long section drowsiness level D serving as the reference is 4 and the first-order short section drowsiness level B is 5, the multiple is 1.25. When the first-order short section drowsiness level B is 7, the multiple is 1.75. Also in this case, the threshold for the estimation that the drowsiness level increases can be set under the concept similar to that for the case of 100 levels.

103 103 103 103 103 e e e f e The drowsiness level change estimation unitoutputs “1” when the drowsiness level increase index value is equal to or larger than the threshold and estimation is made that the drowsiness level increases, and outputs “0” when the drowsiness level increase index value is smaller than the threshold and estimation is made that the drowsiness level does not increase. The drowsiness level change estimation unitmay output the drowsiness level increase index value as the estimation result. The value output from the drowsiness level change estimation unitis input to the drowsiness level information output unit. When the drowsiness level increase index value is smaller than a threshold smaller than 1, the drowsiness level change estimation unitmay estimate that the drowsiness level decreases.

103 1 103 103 e e e When estimating the increase in the drowsiness level, the drowsiness level change estimation unituses, as the target of comparison, the long section drowsiness level D calculated based on the measurement data, and does not use a preset value. This is because the normal drowsiness level differs depending on users and accurate estimation cannot be made for a large number of unspecified users if the preset value is used for the estimation of the increase in the drowsiness level. If the increase in the drowsiness level cannot be estimated accurately, the apparatus including the drowsiness level estimation devicemay excessively perform the apparatus control or may hardly perform the apparatus control. Therefore, the drowsiness level change estimation unituses the long section drowsiness level D for the estimation of the increase in the drowsiness level. Thus, the drowsiness level change estimation unitcan perform appropriate estimation even for unspecified measurement users.

103 103 103 103 103 103 1 f f e e f e As described above, the drowsiness level information output unitmay output any one of the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C as the drowsiness level information, or may output part or all of them. The drowsiness level information output unitoutputs drowsiness level information showing that the drowsiness level increases when “1” is input from the drowsiness level change estimation unit, and outputs drowsiness level information showing that the drowsiness level does not increase when “0” is input from the drowsiness level change estimation unit. As an additional method, the drowsiness level information output unitmay set a small threshold to further increase the estimation accuracy, count the number of times “1” is input from the drowsiness level change estimation unit, and determine that the drowsiness level increases when the observed count is plural. Thus, the apparatus including the drowsiness level estimation devicesuppresses the frequent control change and improves the control accuracy.

103 103 103 e f f When the drowsiness level increase index value is output from the drowsiness level change estimation unit, the drowsiness level information output unitmay output the drowsiness level increase index value as the drowsiness level information. As described above, the drowsiness level increase index value is 1 when the drowsiness level does not change, and is away from 1.0 as the degree of the increase in the drowsiness level increases. For example, when the multiple of the first-order short section drowsiness level B relative to the long section drowsiness level D is 2, the increase index value is 2.0. Therefore, the increase index value is larger than 1.0 when the drowsiness level increases. Thus, the drowsiness level information output unitcan output the degree of the increase in the drowsiness level as the drowsiness level information using the drowsiness level increase index value.

15 FIG. 15 FIG. 103 1 103 11 103 11 12 103 11 12 13 103 14 15 103 is a flowchart illustrating an outline of the process in the analysis unitof the drowsiness level estimation deviceaccording to Embodiment 3. The analysis unitcalculates a single-point drowsiness level A (Step S). Then, the analysis unitcalculates a first-order short section drowsiness level B based on time-series data of the single-point drowsiness levels A calculated in Step S(Step S). The analysis unitcalculates a long section drowsiness level D based on time-series data of the single-point drowsiness levels A calculated in Step Sor the first-order short section drowsiness levels B calculated in Step S(Step S). The analysis unitestimates an increase in the drowsiness level in the manner described above based on the first-order short section drowsiness level B and the long section drowsiness level D (Step S), and outputs drowsiness level information (Step S). In the flowchart of, the calculation of the second-order short section drowsiness level C is omitted. When the second-order short section drowsiness level C is used for the estimation of the increase in the drowsiness level, the analysis unitcalculates the second-order short section drowsiness level C.

103 103 1 As described above, the analysis unitmay output, as the drowsiness level information, part or all of the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C, or information as to whether the drowsiness level increases. The analysis unitcan change the information to be output as appropriate depending on the apparatus including the drowsiness level estimation device.

103 103 103 The analysis unitmay output drowsiness level information showing whether the drowsiness level is high or low based on any one drowsiness level out of the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C. For example, the analysis unitmay perform the following to estimate whether the drowsiness level is high or low. In a case where the drowsiness level is expressed by a grade value and the value increases as the drowsiness level increases, the analysis unitmay estimate that the drowsiness level is high when the estimated drowsiness level is larger than a threshold, and may estimate that the drowsiness level is low when the estimated drowsiness level is smaller than or equal to the threshold.

103 10 103 The analysis unitdescribed above can output the drowsiness level information every 1 second after the measurement of the pulse wave of the user is started by the Doppler sensorand the single-point drowsiness level A is calculated firstly. The analysis unitmay set the timing to output the drowsiness level information to a timing after the interval of 1 second or to a set timing.

103 2 2 2 2 103 2 103 103 For example, when repeatedly outputting the drowsiness level information as to whether the drowsiness level increases, the analysis unitmay set the time Tas the set timing and output the drowsiness level information at intervals of the time T. As described above, the short section drowsiness level is necessary for the estimation as to whether the drowsiness level increases, and the time Tis a time necessary for the acquisition of the time-series data of the single-point drowsiness levels A necessary for the calculation of the first-order short section drowsiness level B. In other words, the time Tis the minimum time necessary for the calculation of the first-order short section drowsiness level B. When the analysis unitoutputs the drowsiness level information at intervals of the time T, the analysis unitcan output estimation results of the change in the drowsiness level based on the first-order short section drowsiness levels B calculated with all the time-series data of the single-point drowsiness levels A replaced every time. When repeatedly estimating the increase in the drowsiness level, the analysis unitcan accurately estimate a decrease in the drowsiness level using the first-order short section drowsiness levels B calculated with all the time-series data of the single-point drowsiness levels A replaced every time.

103 In the above, the analysis unitcan adjust the timing to output the drowsiness level information. However, the drowsiness level information may be output every time the drowsiness level is estimated, and the apparatus that acquires the drowsiness level information may select necessary drowsiness level information and use it for the apparatus control.

1 1 As described above, the drowsiness level estimation deviceof Embodiment 3 can obtain the advantageous effects similar to those of Embodiment 1 or 2, and output not only the single-point drowsiness level A but also the first-order short section drowsiness level B or the second-order short section drowsiness level C as the drowsiness level information. The drowsiness level estimation devicecan estimate the change in the drowsiness level based on the short section drowsiness level and the long section drowsiness level for each user, and accurately output the drowsiness level information showing the estimation result in real time at an appropriate frequency.

1 Embodiment 4 relates to an apparatus including any one of the drowsiness level estimation devicesof Embodiments 1 to 3. In particular, description is made about a case where the apparatus is an air-conditioning apparatus.

16 FIG. 201 201 271 is a diagram illustrating the configuration of an air-conditioning apparatusaccording to Embodiment 4. The air-conditioning apparatusis equipment that conditions air in an indoor spacethat is an air-conditioning target space. Air-conditioning refers to adjustment of, for example, the temperature, humidity, cleanliness, and flow of air in the air-conditioning target space, and is specifically heating, cooling, dehumidifying, humidifying, and air cleaning.

16 FIG. 201 203 201 201 As illustrated in, the air-conditioning apparatusis installed in a building. The air-conditioning apparatusis heat-pump air-conditioning equipment that uses, for example, hydrofluorocarbon (HFC) as refrigerant. The air-conditioning apparatusincludes a vapor compression refrigerant circuit, and operates by electric power supplied from, for example, a commercial power supply, power generation equipment, or power storage equipment (not illustrated).

16 FIG. 201 211 203 213 203 255 211 213 261 263 201 271 213 271 As illustrated in, the air-conditioning apparatusincludes an outdoor unitprovided outside the building, an indoor unitprovided inside the building, and a remote controllerto be operated by a user. The outdoor unitand the indoor unitare connected via a refrigerant pipethrough which the refrigerant flows and a communication linethrough which various signals are transferred. The air-conditioning apparatuscools the indoor spaceby blowing conditioned air such as cold air from the indoor unit, and heats the indoor spaceby blowing hot air.

211 221 222 223 224 231 251 213 225 233 252 256 261 221 222 223 224 225 201 221 222 223 224 225 261 The outdoor unitincludes a compressor, a four-way valve, an outdoor heat exchanger, an expansion valve, an outdoor fan, and an outdoor unit controller. The indoor unitincludes an indoor heat exchanger, an indoor fan, an indoor unit controller, and a human detection sensor. The refrigerant pipeannularly connects the compressor, the four-way valve, the outdoor heat exchanger, the expansion valve, and the indoor heat exchanger. The air-conditioning apparatusincludes a refrigerant circuit defined by connecting the compressor, the four-way valve, the outdoor heat exchanger, the expansion valve, and the indoor heat exchangerby the refrigerant pipe. The refrigerant circulates through the refrigerant circuit and operations of a refrigeration cycle are performed.

221 261 221 222 221 221 221 251 The compressorcompresses the refrigerant and circulates it through the refrigerant pipe. Specifically, the compressorcompresses low-temperature and low-pressure refrigerant, and discharges high-pressure and high-temperature refrigerant to the four-way valve. The compressorincludes an inverter circuit that can change an operation capacity depending on a driving frequency. The operation capacity refers to an amount of refrigerant sent out by the compressorper unit time. The compressorchanges the operation capacity in response to instructions from the outdoor unit controller.

222 221 222 261 201 224 223 225 261 224 224 251 The four-way valveis installed on a discharge side of the compressor. The four-way valvechanges the refrigerant flow direction in the refrigerant pipedepending on whether the operation of the air-conditioning apparatusis a cooling or dehumidifying operation or a heating operation. The expansion valveis installed between the outdoor heat exchangerand the indoor heat exchanger, and expands the refrigerant flowing through the refrigerant pipeby reducing pressure. The expansion valveis an electronic expansion valve that can be controlled so that its opening degree can be changed. The expansion valveadjusts the pressure of the refrigerant by changing the opening degree in response to instructions from the outdoor unit controller.

223 261 272 271 231 223 272 223 223 261 272 The outdoor heat exchangerexchanges heat between the refrigerant flowing through the refrigerant pipeand air in an outdoor space (external space)outside the indoor space. The outdoor fanis provided near the outdoor heat exchanger, sucks air in the outdoor space, and sends the sucked air to the outdoor heat exchanger. The air sent to the outdoor heat exchangerexchanges heat with the refrigerant flowing through the refrigerant pipe, and is then blown to the outdoor space.

225 261 271 233 225 271 225 225 261 271 225 271 271 The indoor heat exchangerexchanges heat between the refrigerant flowing through the refrigerant pipeand air in the indoor space. The indoor fanis provided near the indoor heat exchanger, sucks air in the indoor space, and sends the sucked air to the indoor heat exchanger. The air sent to the indoor heat exchangerexchanges heat with the refrigerant flowing through the refrigerant pipe, and is then blown to the indoor space. The air that exchanges heat in the indoor heat exchangeris supplied to the indoor spaceas conditioned air. Thus, the air in the indoor spaceis conditioned.

251 211 252 213 The outdoor unit controllercontrols the operation of the outdoor unit. The indoor unit controllercontrols the operation of the indoor unit.

255 271 255 252 213 255 255 255 255 255 255 201 201 a a 17 FIG. The remote controlleris located in the indoor space. The remote controllertransmits and receives various signals with the indoor unit controllerof the indoor unit. The remote controllerincludes a display unitdescribed later as illustrated in. The display unitincludes a touchscreen, a liquid crystal display, and light emitting diodes (LEDs). The remote controllerhas push buttons (not illustrated). The remote controllerfunctions as a command reception unit that receives various commands from the user, and a display unit that displays various types of information for the user. The user operates the remote controllerto input commands to the air-conditioning apparatus. Examples of the commands include switching commands for operation and stop, and switching commands for operation modes, set temperature, set humidity, air volumes, air directions, and a timer. The air-conditioning apparatusoperates in response to the input commands.

17 FIG. 201 201 250 280 1 290 201 is a block diagram of the air-conditioning apparatusaccording to Embodiment 4. The air-conditioning apparatusincludes a controller, an air-conditioning unit, and any one of the drowsiness level estimation devicesof Embodiments 1 to 3. An information apparatusto be operated by the user is connected to the air-conditioning apparatusvia a network N.

250 201 250 280 1 250 251 252 250 104 105 104 105 251 252 17 FIG. The controllercontrols the overall air-conditioning apparatus. The controllercontrols the operation of an apparatus body, in other words, the air-conditioning unitbased on drowsiness level information output from the drowsiness level estimation device. The controllerincludes the outdoor unit controllerand the indoor unit controllerdescribed above. Although illustration is omitted in, the controllerincludes the control detail determination unitand the apparatus control unitdescribed in Embodiment 1. The control detail determination unitand the apparatus control unitmay be provided to either of the outdoor unit controllerand the indoor unit controller.

251 251 251 251 251 a b c d The outdoor unit controllerincludes a control unit, a storage unit, a timer unit, and a communication unit. Those units are connected via a bus (not illustrated).

251 251 251 251 252 263 a b c d 16 FIG. The control unitcontrols the overall outdoor unit. The storage unitis a memory such as a RAM or a ROM, and stores data necessary for control. The timer unitmeasures time. The communication unitis an interface for communication with the indoor unit controllervia the communication line(see).

16 FIG. 251 252 263 251 252 252 263 As illustrated in, the outdoor unit controlleris connected to the indoor unit controllerby the communication line. The outdoor unit controllercooperates with the indoor unit controllerby receiving various signals from the indoor unit controllervia the communication line.

252 252 251 255 252 251 255 252 290 252 255 255 252 252 255 a a a a a The indoor unit controllerincludes a communication unitthat communicates with the outdoor unit controllerand the remote controller. The communication unitis an interface for communication with the outdoor unit controllerand the remote controller. The communication unitis further connected to the information apparatusvia the network N. The communication unitperforms a process of receiving various user commands from the remote controller, and a process of transmitting the various commands received from the remote controllerto the indoor unit controller. The communication unitperforms a process of transmitting notification information for the user to the remote controller.

251 252 251 252 251 252 251 252 251 252 250 251 252 211 213 251 252 The outdoor unit controllerand the indoor unit controllerare microprocessor units. Each of the outdoor unit controllerand the indoor unit controllerincludes a central processing unit (CPU), a read only memory (ROM), and a random access memory (RAM). The ROM stores a control program or other data. The outdoor unit controllerand the indoor unit controllerare not limited to the microprocessor units. For example, each of the outdoor unit controllerand the indoor unit controllermay be firmware that can be updated. Each of the outdoor unit controllerand the indoor unit controllermay be a program module to be executed by a command from a CPU (not illustrated) or other devices. Although the description has been made about the example in which the controllerincludes the outdoor unit controllerand the indoor unit controllerthat are separately provided in the outdoor unitand the indoor unit, respectively, the outdoor unit controllerand the indoor unit controllermay be provided as a single control unit having their functions.

280 271 231 233 16 FIG. The air-conditioning unitconditions air in the indoor space, and corresponds to the refrigerant circuit, the outdoor fan, and the indoor fanin.

201 280 1 250 201 280 250 233 The air-conditioning apparatusconfigured as described above controls the operation of the air-conditioning unitbased on drowsiness level information output from the drowsiness level estimation device. Specifically, when the drowsiness level information shows that the drowsiness level is high, the controllerof the air-conditioning apparatuscontrols the air-conditioning unitto perform an operation of arousing the user. Hitherto, it has been ascertained that the user is aroused by wind. Therefore, when the drowsiness level of the user is high, the controllerincreases the air sending amount by, for example, increasing the rotation speed of the indoor fanas an arousing operation for arousing the user.

201 256 250 280 256 250 213 201 The air-conditioning apparatusincludes the human detection sensorthat detects the position of the user. When the drowsiness level of the user is high, the controllercontrols the air-conditioning unitbased on the position of the user detected by the human detection sensorto perform the following arousing operation for arousing the user. The controllercontrols a swing operation for vertically moving a vertical airflow direction flap (not illustrated) provided to the indoor unitso that the user is intermittently exposed to airflow, or controls a swing operation for laterally moving a lateral airflow direction flap (not illustrated) so that the user is intermittently exposed to airflow. Through the above control, the air-conditioning apparatuscan arouse the user with the high drowsiness level.

250 The room temperature is preferably lower by about 1 degree Celsius than the temperature that the user feels as an optimum temperature because of an effect that the brain is cooled. Thus, the work efficiency is high. Particularly during heating, the drowsiness level increases when the set temperature is excessively high. Therefore, the controllermay perform, as the arousing operation, control for adjusting the set indoor temperature to a temperature slightly lower than the current temperature.

250 233 250 201 201 When the drowsiness level is conversely low, the controllerreduces the air sending amount by reducing the rotation speed of the indoor fan, or controls the vertical airflow direction flap to keep an upward posture to the extent possible so that the user is not exposed to airflow. The controllercontrols either or both of the vertical airflow direction flap (not illustrated) and the lateral airflow direction flap (not illustrated) so that the user is not exposed to airflow. Through the above control, the air-conditioning apparatuscan reduce the case where the concentration level decreases because the user exposed to airflow turns attention to the airflow. In other words, the air-conditioning apparatuscan obtain an advantageous effect in that the user with the low drowsiness level and the high concentration level can work without paying attention to the airflow.

1 1 1 201 280 201 201 201 When the drowsiness level estimation deviceis the drowsiness level estimation deviceof Embodiment 3, the drowsiness level shown by the drowsiness level information output from the drowsiness level estimation deviceincludes the single-point drowsiness level A, the first-order short section drowsiness level B, and the second-order short section drowsiness level C. If the air-conditioning apparatuscontrols the operation of the air-conditioning unitbased on the single-point drowsiness level A, that is, the drowsiness level of the user at a certain time point, the control of the air-conditioning apparatusmay be changed frequently. In this case, the user may be annoyed with the operation of the air-conditioning apparatusor dissatisfied frequently with the change in temperature or airflow. In view of this, the air-conditioning apparatususes the first-order short section drowsiness level B or the second-order short section drowsiness level C to perform control based on the drowsiness level of the user. Thus, there is an advantage in that the control can be determined at an appropriate frequency with the tendency reflected accurately.

201 201 201 However, the second-order short section drowsiness level C is a value calculated by performing the averaging process twice. Therefore, when the air-conditioning apparatusperforms control based on the second-order short section drowsiness level C, it is difficult to perform control with a quick response to fine drowsiness level increases compared with the control based on the first-order short section drowsiness level B. Since the second-order short section drowsiness level C is a value calculated by performing the averaging process twice, the calculation time is required compared with the first-order short section drowsiness level B. Therefore, when the second-order short section drowsiness level C is used as the drowsiness level of the user, the air-conditioning apparatusrequires time for the control change estimation. Thus, the air-conditioning apparatuspreferably selects the first-order short section drowsiness level B or the second-order short section drowsiness level C depending on a desired control change frequency, response, and estimation time.

201 280 18 FIG. The air-conditioning apparatuscan also control the operation of the air-conditioning unitto perform the arousing operation in response to the change that is the increase in the drowsiness level. The control in this case is described with reference to.

18 FIG. 201 2 2 201 1 31 201 2 1 1 2 3 4 2 1 is a flowchart illustrating the operation of the air-conditioning apparatusaccording to Embodiment 4. In the following description, the time Tis the same as the time Tin Embodiment 3. The air-conditioning apparatusacquires drowsiness level information output from the drowsiness level estimation device(Step S). The air-conditioning apparatusacquires drowsiness level information output at intervals of the time Tfrom the drowsiness level estimation device. In the following description, drowsiness level information R, drowsiness level information R, drowsiness level information R, and drowsiness level information Rare first, second, third, and fourth pieces of drowsiness level information output at intervals of the time Tfrom the drowsiness level estimation devicein this order.

201 1 31 32 1 201 33 280 34 201 35 201 The air-conditioning apparatusdetermines whether the drowsiness level information Racquired in Step Sshows that the drowsiness level of the user increases (Step S). When the drowsiness level information Rshows that the drowsiness level increases, the air-conditioning apparatuscontrols, through Step Sdescribed later, the air-conditioning unitto start an arousing operation for arousing the user (Step S). As described above, the arousing operation is the swing operation for vertically moving the vertical airflow direction flap. Then, the air-conditioning apparatuscounts the number of times the arousing operation is performed (Step S). In this case, the air-conditioning apparatuscounts “1.”

201 2 2 36 201 2 37 2 201 38 201 34 201 34 201 201 35 The air-conditioning apparatuscontinues the arousing operation until the next drowsiness level information Ris acquired. When the drowsiness level information Ris newly acquired (Step S), the air-conditioning apparatusdetermines whether the drowsiness level information Rshows that the drowsiness level of the user increases (Step S). When the drowsiness level information Rshows that the drowsiness level still increases, the air-conditioning apparatusdetermines whether the count is smaller than a set count (Step S). If the set count is “3,” the current count is “1” and is smaller than the set count. Therefore, the air-conditioning apparatusreturns to Step Sand continues the arousing operation. The air-conditioning apparatusmay continue the arousing operation at the same air volume as that when the arousing operation was started previously in Step S, or may increase the air volume to arouse the user more intensely. The air-conditioning apparatuspreferably increases the air volume because the drowsiness level of the user can be reduced quickly. Then, the air-conditioning apparatuscounts “2” as the number of times the arousing operation is performed (Step S).

201 3 3 36 201 3 37 3 201 38 201 34 201 34 201 35 The air-conditioning apparatuscontinues the arousing operation until the next drowsiness level information Ris acquired. When the drowsiness level information Ris newly acquired (Step S), the air-conditioning apparatusdetermines whether the drowsiness level information Rshows that the drowsiness level of the user increases (Step S). When the drowsiness level information Rshows that the drowsiness level still increases, the air-conditioning apparatusdetermines whether the count is smaller than the set count (Step S). The current count is “2” and is smaller than the set count “3.” Therefore, the air-conditioning apparatusreturns to Step Sand continues the arousing operation. The air-conditioning apparatusmay continue the arousing operation at the same air volume as that when the arousing operation was continued previously in Step S, or may increase the air volume to arouse the user more intensely. Then, the air-conditioning apparatuscounts “3” as the number of times the arousing operation is performed (Step S).

201 4 4 36 201 4 37 4 201 38 201 39 The air-conditioning apparatuscontinues the arousing operation until the next drowsiness level information Ris acquired. When the drowsiness level information Ris newly acquired (Step S), the air-conditioning apparatusdetermines whether the drowsiness level information Rshows that the drowsiness level of the user increases (Step S). When the drowsiness level information Rshows that the drowsiness level still increases, the air-conditioning apparatusdetermines whether the count is smaller than the set count (Step S). The count is “3” and is not smaller than the set count. Therefore, the air-conditioning apparatusstops the arousing operation (Step S).

201 201 That is, when the drowsiness level of the user does not decrease though the set count of arousing operations is performed, the air-conditioning apparatusstops the arousing operation because the user may be dissatisfied with the feeling of airflow. Although the set count is “3,” the set count is not limited to “3.” The air-conditioning apparatusperforms the swing operation as the arousing operation, but may perform control for reducing the room temperature to cool the brain though the control requires time to arouse the user. That is, the above arousing operation includes the operation of reducing the room temperature as well as the swing operation.

2 3 37 201 34 39 When the drowsiness level information Ror Rdoes not show that the drowsiness level of the user increases, that is, the user is aroused in Step S, the air-conditioning apparatusstops the arousing operation without returning to the arousing operation in Step S(Step S).

33 201 32 201 201 201 Step Sis described. When the air-conditioning apparatusestimates in Step Sthat the drowsiness level increases, the air-conditioning apparatusdoes not immediately perform the arousing operation, but performs the arousing operation when determination is made that a non-operating time has elapsed from the previous arousing operation. That is, the air-conditioning apparatussets the non-operating time so that the arousing operation is not performed for a predetermined time after the arousing operation has once been performed in response to the estimation that the drowsiness level increases. This is because the user is kept aroused after he/she has been aroused by the arousing operation of the air-conditioning apparatus.

201 201 If the non-operating time is not provided, the air-conditioning apparatusmay frequently expose the user to airflow, and the comfort may be impaired. With the non-operating time, the air-conditioning apparatuscan avoid the user's dissatisfaction with airflow or thermal sensation. For example, the non-operating time is preferably 10 minutes to 15 minutes because a decrease in the drowsiness level is observed clearly.

201 280 1 201 201 As described above, the air-conditioning apparatuscan accurately perform the arousing operation at an appropriate frequency depending on the drowsiness level of the user by controlling the air-conditioning unitusing the drowsiness level information output from the drowsiness level estimation device. As a result, the air-conditioning apparatuscan maintain the work efficiency of the user during work or study. Alternatively, the air-conditioning apparatushas an advantageous effect in that the work efficiency can be improved by suppressing the increase in the drowsiness level of the user.

255 201 290 290 291 291 290 290 290 255 The remote controlleris part of the components of the air-conditioning apparatus, and the information apparatusis an apparatus owned by the user. The information apparatusincludes a display unitsuch as a liquid crystal panel. The display unitdisplays various types of information. The information apparatusis, for example, a smartphone or a tablet. An application for displaying the drowsiness level of the user or other information is installed in the information apparatus. An air-conditioning control application may be installed and the information apparatusmay be used in place of the remote controller.

290 290 1 291 250 201 290 252 1 290 290 290 255 255 a a When the information apparatusis operated by the user, the information apparatusstarts the application, acquires drowsiness level information output from the drowsiness level estimation devicevia the network N, and displays it on the display unit. As specific control, the controllerof the air-conditioning apparatusperforms a process of transmitting, to the information apparatusvia the communication unit, drowsiness level information output by the drowsiness level estimation devicein response to a request from the information apparatus, and displaying the drowsiness level information on the information apparatus. Although the drowsiness level information is displayed on the information apparatus, the drowsiness level information may be displayed on the display unitof the remote controller.

201 290 255 255 a As described above, the air-conditioning apparatusvisualizes the drowsiness level information by displaying it on the information apparatusor the display unitof the remote controller. Thus, the user can view the drowsiness level.

1 201 201 1 1 Although the description has been made that the apparatus including the drowsiness level estimation deviceis the air-conditioning apparatus, the apparatus is not limited to the air-conditioning apparatus, and the drowsiness level estimation devicecan be provided to an electrical apparatus, a vehicle, an amusement apparatus, or various other apparatuses. The drowsiness level estimation devicecan also be provided to, for example, a labor management apparatus or a learning management apparatus.

1 1 1 The drowsiness level estimation devicecan be provided to an apparatus in a wide variety of fields such as healthcare, labor, education, sleep, mindfulness, meditation, customer services, marketing, or sport mental training. When the drowsiness level estimation deviceis provided to such an apparatus, the apparatus can perform the apparatus control using the drowsiness level estimation result and present the drowsiness level estimation result to the user. When the drowsiness level estimation deviceis applied to various apparatuses, the drowsiness level can be visualized and presented to the user of the apparatus.